71 research outputs found

    A Simple RVoG Test for PolInSAR Data

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    In this paper, we present a simple algorithm for assessing the validity of the RVoG model for PolInSAR-based inversion techniques. This approach makes use of two important features characterizing a homogeneous random volume over a ground surface, i.e., the independence on polarization states of wave propagation through the volume and the structure of the polarimetric interferometric coherency matrix. These two features have led to two different methods proposed in the literature for retrieving the topographic phase within natural covers, i.e., the well-known line fitting procedure and the observation of the (1, 2) element of the polarimetric interferometric coherency matrix. We show that differences between outputs from both approaches can be interpreted in terms of the PolInSAR modeling based on the Freeman-Durden concept, and this leads to the definition of a RVoG/non-RVoG test. The algorithm is tested with both indoor and airborne data over agricultural and tropical forest areas.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Project TEC2011-28201-C02-02

    Retrieval of vegetation height in rice fields using polarimetric SAR interferometry with TanDEM-X data

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    This work presents for the first time a demonstration with satellite data of polarimetric SAR interferometry (PolInSAR) applied to the retrieval of vegetation height in rice fields. Three series of dual-pol interferometric SAR data acquired with large baselines (2โ€“3 km) by the TanDEM-X system during its science phase (Aprilโ€“September 2015) are exploited. A novel inversion algorithm especially suited for rice fields cultivated in flooded soil is proposed and evaluated. The validation is carried out over three test sites located in geographically different areas: Sevilla (SW Spain), Valencia (E Spain), and Ipsala (W Turkey), in which different rice types are present. Results are obtained during the whole growth cycle and demonstrate that PolInSAR is useful to produce accurate height estimates (RMSE 10โ€“20 cm) when plants are tall enough (taller than 25โ€“40 cm), without relying on external reference information.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under project TIN2014-55413-C2-2-P. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007โ€“2013) under grant agreement 606983, and the Land-SAF (the EUMETSAT Network of Satellite Application Facilities) project. The in-situ measurements in the Ipsala site were conducted with the funding of The Scientific and Technological Research Council of Turkey (TUBITAK, Project No.: 113Y446)

    Estimation of RVoG Scene Parameters by Means of PolInSAR With TanDEM-X Data: Effect of the Double-Bounce Contribution

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    This article evaluates the effect of the double-bounce (DB) decorrelation term that appears in single-pass bistatic acquisitions, as in the TanDEM-X system, on the inversion of scene parameters by means of polarimetric SAR interferometry (PolInSAR). The retrieval of all scene parameters involved in the Random Volume over Ground (RVoG) model (i.e., ground topography, vegetation height, extinction, and ground-to-volume ratios) is affected by this term when the radar response from the ground is dominated by the DB. The estimation error in all these parameters is analyzed by means of simulations over a wide range of system configurations and scene variables for both agricultural crops and forest scenarios. Simulations demonstrate that the inclusion of the DB term, which complicates the inversion algorithm, is necessary for the angles of incidence shallower than 30ยฐ to achieve an estimation error below 10% in vegetation height and to avoid a significant underestimation in the ground-to-volume ratios. At steep incidences, this decorrelation term does not affect the estimation of vegetation height and ground-to-volume ratios. Regarding the extinction, this parameter is intrinsically not well estimated, since most retrieved values are close to the initial guesses employed for the optimization algorithm, regardless of the use or not of the DB decorrelation term. Finally, these findings are compared with the experimental results from the TanDEM-X data acquired over the rice fields in Spain for the available system parameters (baseline and incidence angle) of the acquired data set.This work was supported in part by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI), and in part by the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P. The work of Noelia Romero-Puig was supported in part by the Generalitat Valenciana and in part by the European Social Fund (ESF) under Grant ACIF/2018/204

    Assessment of Paddy Rice Height: Sequential Inversion of Coherent and Incoherent Model

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    This paper investigates the evolution of canopy height of rice fields for a complete growth cycle. For this purpose, copolar interferometric Synthetic Aperture Radar (Pol-InSAR) time series data were acquired during the large across-track baseline (>1 km) science phase of the TanDEM-X mission. The height of rice canopies is estimated by three different model-based approaches. The first approach evaluates the inversion of the Random Volume over Ground (RVoG) model. The second approach evaluates the inversion of a metamodel-driven electromagnetic backscattering model by including a priori morphological information. The third approach combines the previous two processes. The validation analysis was carried out using the Pol-InSAR and ground measurement data acquired between May and September in 2015 over rice fields located in Ipsala district of Edirne, Turkey. The results of presented height estimation algorithms demonstrated the advantage of Pol-InSAR data. The combined RvoG model and EM metamodel height estimation approach provided rice canopy heights with errors less than 20 cm for the complete growth cycle

    A Review of Crop Height Retrieval Using InSAR Strategies: Techniques and Challenges

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    This article compares the performance of four different interferometric synthetic aperture radar (SAR) techniques for the estimation of rice crop height by means of bistatic TanDEM-X data. Methods based on the interferometric phase alone, on the coherence amplitude alone, on the complex coherence value, and on polarimetric SAR interferometry (PolInSAR) are analyzed. Validation is conducted with reference data acquired over rice fields in Spain during the Science Phase of the TanDEM-X mission. Single- and dual-polarized data are exploited to also provide further insights into the polarization influence on these approaches. Vegetation height estimates from methodologies based on the interferometric phase show a general underestimation for the HH channel (with a bias that reaches around 25 cm in mid-July for some fields), whereas the VV channel is strongly influenced by noisy phases, especially at large incidences [root-mean-square error (RMSE) = 31 cm]. Results show that these approaches perform better at shallower incidences than the methodologies based on coherence amplitude and on PolInSAR, which obtain the most suitable results at steep incidences, with RMSE values of 17 and 23 cm. On the contrary, at shallower incidences, they are highly affected by very low input coherence levels. Hence, they tend to overestimate vegetation height.This work was supported by the Spanish Ministry of Science and Innovation, in part by the State Agency of Research, and in part by the European Funds for Regional Development under Project TEC2017-85244-C2-1-P. The work of Noelia Romero-Puig was supported in part by the Generalitat Valenciana and in part by the European Social Fund under Grant ACIF/2018/204

    Assessment of L-Band SAOCOM InSAR coherence and its comparison with C-Band: A case study over managed forests in Argentina

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    The objective of this work is to analyze the behavior of short temporal baseline interferomet ric coherence in forested areas for L-band spaceborne SAR data. Hence, an exploratory assessment of the impacts of temporal and spatial baselines on coherence, with emphasis on how these effects vary between SAOCOM-1 L-band and Sentinel-1 C-band data is presented. The interferometric coherence is analyzed according to different imaging parameters. In the case of SAOCOM-1, the impacts of the variation of the incidence angle and the ascending and descending orbits over forested areas are also assessed. Finally, short-term 8-day interferometric coherence maps derived from SAOCOM-1 are especially addressed, since this is the first L-band spaceborne mission that allows us to acquire SAR images with such a short temporal span. The analysis is reported over two forest-production areas in Argentina, one of which is part of the most important region in terms of forest plantations at the national level. In the case of SAOCOM, interferometric configurations are characterized by a lack of control on the spatial baseline, so a zero-baseline orbital tube cannot be guaranteed. Nevertheless, this spatial baseline variability is crucial to exploit volume decorrelation for forest monitoring. The results from this exploratory analysis demonstrates that SAOCOM-1 short temporal baseline interferograms, 8 to 16 days, must be considered in order to mitigate temporal decorrelation effects and to be able to experiment with different spatial baseline configurations, in order to allow appropriate forest monitoring.This research was funded by the project INTERACT PID2020-114623RB-C32 funded by the Spanish MCIN /AEI /10.13039 /501100011033.Peer ReviewedPostprint (published version

    PolSAR and PolInSAR model based information estimation

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    Speckle for multidimensional SAR data may be modeled as the combination of multiplicative and additive noise sources. As demonstrated, the use of this noise model does not corrupt the estimation of physical information from PolInSAR data. The definition of a model based PolInSAR filter allows also the computation of relative errors for estimated heights of forested areas from PolInSAR data.Peer ReviewedPostprint (published version

    Hemiboreaalsete metsade kaardistamine interferomeetrilise tehisava-radari andmetelt

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    Vรคitekirja elektrooniline versioon ei sisalda publikatsioone.Kรคesolev doktoritรถรถ uurib tehisavaradari (SAR) kasutusvรตimalusi metsa kรตrguse hindamiseks hemiboreaalsete metsade vรถรถndis. Uurimistรถรถ viidi lรคbi Tartu รœliยฌkooli, Tartu Observatooriumi, Aalto รœlikooli, Euroopa Kosmoseagentuuri (ESA) kaugseire keskuse ESRIN ja Reach-U koostรถรถs. Uurimistรถรถs kasutatud satelliidiยฌandmed on pรคrit Saksa Kosmosekeskuse (DLR) kรตrglahutusega bistaatilise X-laineala tehisavaradari TanDEM-X satelliidipaarilt. Sagedasti uuenevad satelliidiandmed, nende globaalne katvus ja kรตrge ruumiยฌline lahutus vรตimaldavad tehisavaradari abil kaardistada metsi ning nendes toimuยฌvaid muutusi suurtel maa-aladel. Radari abil on vรตimalik saada kรตrge lahutusvรตimega pilte, mis on tundlikud taimestikule, maapinna karedusele ja dielektrilistele omadustele. Sรผnkroonis lendava radaripaari samaaegselt tehtud pildid elimineerivad vรตimalikud ajalised muutused taimestikus ning tรคnu sellele on radariandmetest vรตimalik tuletada metsade vertikaalset struktuuri ja kรตrgust. Uurimistรถรถs kรคsitletakse tehisavaradari interferomeetrilise koherentsuse tundยฌlikkust metsa kรตrguse suhtes ning analรผรผsitakse, millised keskkonna ja klimaatiยฌlised tingimused ning satelliidi orbiidiga seotud parameetrid mรตjutavad radariยฌpiltidelt erinevate puuliikide kรตrguse hindamise tรคpsust. Lisaks keskendub vรคitekiri interferomeetrilisele koherentsusele tuginevate mudelite analรผรผsiยฌmisele ning nende tรคpsuse hindamisele operatiivse metsa kรตrguse kaardistamise raken-duseks. Vaatluse alla on vรตetud kolm testala, mis asuvad Soomaa rahvuspargis, Vรตrtsjรคrve idakaldal Rannus ja Peipsiveere looduskaitsealal ning katavad kokku 2291 hektarit metsa. 23 TanDEM-X satelliidipildi koherentsuspilte vรตrreldakse samadel testaladel aerolaserskaneerimise (LiDAR) abil mรตรตdetud puistute kรตrguยฌsega, mis on omakorda jagatud kolme rรผhma (kuused, mรคnnid ja laiaยฌlehised segametsad). RVoG (Random Volume over Ground) taimekatte mudel ning sellest tuleยฌtatud lihtsamad pooleempiirilised mudelid sobituvad olemasolevate TanDEM-X koherentsuse ning LiDARi metsa puistute kรตrgusandmetega hรคsti. Tรถรถ tuleยฌmused kinnitavad, et tulevikus on suurte ja erinevatest metsatรผรผpidest koosneยฌvate metsade kรตrguse kosmosest kaardistamisel otstarbekas kasutusele vรตtta esmalt just soovitatud lihtsamad ja universaalsemad mudelid.This thesis presents research in the field of radar remote sensing and contributes to the forest monitoring application development using space-borne synthetic aperture radar (SAR). Satellite data is particularly useful for large-scale forestry applications making high revisit monitoring of the state of forests worldwide possible. The sensitivity of SAR to the dielectric and geometrical properties of the targets, penetration capacity and coherent imaging properties make it a unique tool for mapping and monitoring forest biomes. SAR satellites are also capable of retrieving additional information about the structure of the forest, tree height and biomass estimates as an essential input for monitoring the changes in the carbon stocks. Interferometric SAR (InSAR) is an advanced SAR imaging technique that allows the retrieval of forest parameters while working in nearly all weather conditions, independently of daylight and cloud cover. This research concenยฌtrates on assessing the impact of different variables affecting hemiboreal forest height estimation from space-borne X-band interferometric SAR coherence data. In particular, the research analyses the changes in coherence dynamics related to seasonal conditions, tree species and imaging properties using a large collection of interferometric SAR images from different seasons over a four-year period. The study is carried out over three test sites in Estonia using the extensive multi-temporal dataset of 23 TanDEM-X images, covering 2291 hectares of forests to describe the relation between the interferometric SAR coherence magยฌnitude and forest parameters. The work demonstrates how the correlation of interferometric coherence and Airborne LiDAR Scanning (ALS)-derived forest height varies for pine and deciduous tree species, for summer (leaf-on) and winter (leaf-off) conditions and for flooded forest floor. A simple semi-empirical modelling approach is proposed as being suitable for wide area forest mapping with limited a priori information under a range of seasonal and environยฌยฌmental conditions. A Random Volume over Ground (RVoG) model and three semi-empirical models are compared and validated against a large dataset of coherence magnitude and ALS-measured data over hemiboreal forests in Estonia. The results show that all proposed models perform well in describing the relationship between hemiboreal forest height and interferometric coherence, allowing in future to derive forest stand height with an accuracy suitable for a wide range of applications

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€, 2017. 8. ๊น€๋•์ง„.์ž์—ฐ ์žฌํ•ด์— ๋Œ€ํ•œ ๋น ๋ฅธ ๋Œ€์‘๊ณผ ๋ณต๊ตฌ๋ฅผ ์œ„ํ•ด์„œ๋Š” ํ”ผํ•ด ์ง€์—ญ์— ๋Œ€ํ•œ ํ‰๊ฐ€๊ฐ€ ์„ ํ–‰๋˜์–ด์•ผ ํ•˜๋ฉฐ, ๊ทธ๋Ÿฐ ์˜๋ฏธ๋กœ ํ”ผํ•ด ์ง€์—ญ์„ ํƒ์ง€ํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. SAR ์‹œ์Šคํ…œ์€ ๊ธฐ์ƒ์  ์กฐ๊ฑด๊ณผ ์ฃผ์•ผ์— ๋ฌด๊ด€ํ•˜๊ฒŒ ์˜์ƒ์„ ํš๋“ํ•  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ, ๋ณ€ํ™” ํ˜น์€ ํ”ผํ•ด ์ง€์—ญ์„ ํƒ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ํšจ์œจ์ ์ธ ๋ฐฉ๋ฒ•์ด๋ผ๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋˜ํ•œ SAR ์‹œ์Šคํ…œ์„ ํ†ตํ•˜์—ฌ ๊ณ„์‚ฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ธด๋ฐ€๋„ (coherence)๋Š” ์ง€ํ‘œ์˜ ์‚ฐ๋ž€์ฒด์˜ ์›€์ง์ž„ ํ˜น์€ ์œ ์ „์  ์„ฑ์งˆ์— ๋ณ€ํ™”์— ๋งค์šฐ ๋ฏผ๊ฐํ•˜๊ฒŒ ๋ฐ˜์‘ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํ”ผํ•ด๋ฅผ ํƒ์ง€ํ•˜๊ธฐ์— ์ ํ•ฉํ•˜๋‹ค๊ณ  ํ‰๊ฐ€๋˜์–ด ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธด๋ฐ€๋„๋ฅผ ์ด์šฉํ•œ ์ž์—ฐ์žฌํ•ด์˜ ํ”ผํ•ด ํƒ์ง€์—๋Š” ์–ด๋ ค์›€์ด ์กด์žฌํ•  ์ˆ˜ ์žˆ๋‹ค. ์ฆ‰, ํƒ์ง€ํ•˜๊ณ ์ž ํ•˜๋Š” ์ž์—ฐ์žฌํ•ด๋กœ ์ธํ•œ ํ”ผํ•ด์™€ ๋น„, ๋ˆˆ, ๋ฐ”๋žŒ๊ณผ ๊ฐ™์€ ๊ธฐ์ƒํ˜„์ƒ, ํ˜น์€ ์‹์ƒ์˜ ์ž์—ฐ์ ์ธ ๋ณ€ํ™”๊ฐ€ ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ๊ธด๋ฐ€๋„์—์„œ๋Š” ์œ ์‚ฌํ•˜๊ฒŒ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๊ฒƒ์€ ๋ ˆ์ด๋” ์‹ ํ˜ธ์˜ ๊ธด๋ฐ€๋„๊ฐ€ ๋ฏธ์„ธํ•œ ๋ณ€ํ™”์—๋„ ๋ฏผ๊ฐํ•˜๊ฒŒ ๋ฐ˜์‘ํ•˜๋Š” ํŠน์ง•์œผ๋กœ๋ถ€ํ„ฐ ๊ธฐ์ธํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ž์—ฐ ํ˜„์ƒ์œผ๋กœ๋ถ€ํ„ฐ ๋ฐœ์ƒํ•˜๋Š” ๊ธด๋ฐ€๋„ ๊ฐ์†Œ ํ˜„์ƒ์€ ํ”ผํ•ด ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ ์˜คํƒ์ง€์œจ์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ์›์ธ์ด ๋˜๋ฉฐ, ์ž์—ฐ ์žฌํ•ด์˜ ์˜ํ–ฅ๊ณผ ๋ถ„๋ฆฌํ•ด์•ผ ํ•  ํ•„์š”์„ฑ์ด ์žˆ๋‹ค. ๋˜ํ•œ ๋‹ค์–‘ํ•œ ์ง€ํ‘œ ํŠน์„ฑ์„ ๊ฐ€์ง€๋Š” ํ”ฝ์…€๋“ค์€ ์ž์—ฐ ํ˜„์ƒ์— ๋Œ€ํ•œ ๊ฐ๊ธฐ ๋‹ค๋ฅธ ๊ธด๋ฐ€๋„ ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์ •ํ™•ํ•œ ํ”ผํ•ด ํƒ์ง€๋ฅผ ์œ„ํ•ด์„œ๋Š” ๊ฐ ํ”ฝ์…€๋“ค์—์„œ์˜ ๋…๋ฆฝ์ ์ธ ํ‰๊ฐ€๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๊ธด๋ฐ€๋„๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ์š”์ธ๋“ค์ด ๋‹ค์–‘ํ•˜๊ณ  ๋ณตํ•ฉ์ ์œผ๋กœ ์ž‘์šฉํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํ•ด์„์— ์–ด๋ ค์›€์ด ์žˆ๋‹ค๋Š” ์  ์—ญ์‹œ ๊ธด๋ฐ€๋„ ๊ธฐ๋ฐ˜ ํ”ผํ•ด ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ•œ๊ณ„์ ์ด๋‹ค. ํŠนํžˆ ์‹์ƒ์ด ์กด์žฌํ•˜๋Š” ์ง€์—ญ์—์„œ์˜ ๊ธด๋ฐ€๋„์˜ ๋ณ€ํ™”๋Š” ๋”์šฑ ๋ณต์žกํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ ์ด์œ ๋Š” ์œ ์ „์  ์„ฑ์งˆ์„ ์ง€๋‹ˆ๊ณ  ์žˆ๋Š” ์‚ฐ๋ž€์ฒด๋“ค์ด ์‹์ƒ์—์„œ๋Š” ์ˆ˜์ง์ ์œผ๋กœ ๋ถ„ํฌํ•˜๋ฉฐ, ํŒŒ์žฅ์ด ๊ธด ๋ ˆ์ด๋” ์‹ ํ˜ธ๊ฐ€ ์ด๋ฅผ ํˆฌ๊ณผํ•จ์— ๋”ฐ๋ผ ์‹์ƒ์˜ ์ƒ์ธต๋ถ€๋ถ€ํ„ฐ ํ•˜์ธต๋ถ€ ๋˜ํ•œ ์ง€ํ‘œ๋ฉด๊นŒ์ง€ ๋„๋‹ฌ๋˜์–ด ์‚ฐ๋ž€๋˜์–ด ๊ธด๋ฐ€๋„๋ฅผ ๊ฐ์†Œ์‹œํ‚ค๋Š” ์ฒด์  ๊ธด๋ฐ€๋„ ๊ฐ์†Œ ํ˜„์ƒ(volume decorrelation) ๋•Œ๋ฌธ์ด๋‹ค. ํš๋“ ์‹œ๊ฐ„์ด ๋™์ผํ•˜์ง€ ์•Š์€ ๋‘ ์žฅ์˜ SAR ์˜์ƒ์„ ์‚ฌ์šฉํ•˜๋Š” repeat-pass ๊ฐ„์„ญ๊ธฐ๋ฒ•์—์„œ๋Š” ๊ฐ ์‹์ƒ์˜ ๊ฐ ๋ถ€๋ถ„์—์„œ ๋ฐœ์ƒ๋˜๋Š” ๋ณ€ํ™” ์ •๋ณด(temporal decorrelation)๋„ ๋™์‹œ์— ๊ธฐ๋ก๋˜๊ธฐ ๋•Œ๋ฌธ์— ํ•ด์„์€ ๋”์šฑ ์–ด๋ ค์›Œ์ง„๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹ค์ค‘ ์‹œ๊ธฐ ๊ธด๋ฐ€๋„๋ฅผ ์ด์šฉํ•˜์—ฌ ์ž์—ฐ ํ˜„์ƒ์„ ํ•ด์„ ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋ธ์„ ์ œ์ž‘ํ•˜๊ณ  ์ด๋ฅผ ๋ณ€ํ™” ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ํ™•์žฅํ•˜์—ฌ, ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ํ‰๊ฐ€ํ•˜๊ณ  ์ •๋ฐ€ํ•œ ํ”ผํ•ด ์ง€์—ญ์„ ์ถ”์ถœํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ ์ฒซ ๋ฒˆ์งธ๋กœ๋Š” ๊ฐ„์„ญ ๊ธฐ๋ฒ•์—์„œ์˜ ์‹œ๊ฐ„ ์ฐจ์ด(temporal baseline)์ด ๊ธธ ๋•Œ, ๋‹ค์ค‘ ์‹œ๊ธฐ ๊ธด๋ฐ€๋„(multi-temporal coherence)๋ฅผ ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋ธ์„ ์ œ์ž‘ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ๋Š” ๋‹จ์ผ ํŽธํŒŒ์˜ ๋‹ค์ค‘ ์‹œ๊ธฐ SAR ์˜์ƒ์—์„œ ๊ด€์ธก๋˜๋Š” ๊ธด๋ฐ€๋„๋ฅผ ํ•ด์„ํ•˜๊ณ , ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์ถ”์ถœํ•˜๋ฉฐ, ๊ฒฐ๊ณผ์ ์œผ๋กœ ํ”ผํ•ด๋ฅผ ํƒ์ง€ํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์„ ๊ธฐ์ˆ ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์„ธ ๋ฒˆ์งธ๋กœ๋Š” ๋‹ค์ค‘ํŽธํŒŒ์˜ ๋‹ค์ค‘ ์‹œ๊ธฐ SAR ์˜์ƒ์— ๋Œ€ํ•œ ํ•ด์„ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. 2์žฅ์—์„œ๋Š” ๊ธด๋ฐ€๋„์˜ ์ธก์ •๊ณผ ๊ธด๋ฐ€๋„๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๋Œ€ํ‘œ์  ์š”์ธ์— ๋Œ€ํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๊ณ  ์‹œ๊ณ„์—ด ๊ธด๋ฐ€๋„ ๊ฐ์†Œ ๋ชจ๋ธ์„ ์ˆ˜์‹ํ™”ํ•˜์˜€๋‹ค. ๊ธด๋ฐ€๋„ ์š”์ธ ์ค‘ ์ฒซ ๋ฒˆ์งธ๋Š” ์—ด์žก์Œ ๊ธด๋ฐ€๋„ ๊ฐ์†Œ(thermal decorrelation)๋กœ์„œ, ์—ด ์žก์Œ (thermal noise)๋กœ๋ถ€ํ„ฐ ๊ธฐ์ธ๋˜๋ฉฐ, ๊ฐ ์‚ฐ๋ž€์ฒด์˜ ์‹ ํ˜ธ๋Œ€ ์žก์Œ๋น„(signal-to-noise ratio)์™€ ๋ฐ€์ ‘ํ•œ ๊ด€๋ จ์ด ์žˆ๋‹ค. ๋‘ ๋ฒˆ์งธ๋Š” ๊ธฐํ•˜ํ•™์  ๋น„์ƒ๊ด€์„ฑ(geometric decorrelation)์œผ๋กœ, ๋‘ ์„ผ์„œ๊ฐ€ ๋‹ค๋ฅธ ์œ„์น˜์—์„œ ์‹ ํ˜ธ๋ฅผ ์†ก์ˆ˜์‹ ํ•  ๋•Œ ์ง€์ƒ์— ํˆฌ์˜๋˜๋Š” ํŒŒ์ˆ˜์˜ ์ŠคํŽ™ํŠธ๋Ÿผ์ด ์ด๋™ํ•จ์— ๋”ฐ๋ผ ๋ฐœ์ƒํ•œ๋‹ค. ์„ธ ๋ฒˆ์งธ ์š”์ธ์€ ์ผ๋ฐ˜์ ์œผ๋กœ ์ฒด์  ๋น„์ƒ๊ด€์„ฑ (volume decorrelation)์ด๋ผ ์–ธ๊ธ‰๋˜๋Š” ๊ฒƒ์œผ๋กœ ์ง€์ƒ์˜ ๋งค์งˆ ์•ˆ์— ์‚ฐ๋ž€์ฒด๊ฐ€ ๋žœ๋คํ•˜๊ฒŒ ๋ถ„ํฌํ•˜๊ณ  ์ „์žํŒŒ๊ฐ€ ์ด๋ฅผ ํˆฌ๊ณผํ•  ๋•Œ ๋ฐœ์ƒํ•˜๋Š” ์œ„์ƒ์ฐจ์ด์— ์˜ํ•˜์—ฌ ๋ฐœ์ƒ๋œ๋‹ค. ์ฒด์  ๋น„์ƒ๊ด€์„ฑ์€ ์‹์ƒ์—์„œ ์ฃผ๋กœ ๊ด€์ฐฐ๋˜๋ฉฐ, ์ด๋ฅผ ์„ค๋ช…ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ RVoG ๋ชจ๋ธ์ด ์ œ์•ˆ๋˜๊ธฐ๋„ ํ•˜์˜€๋‹ค. RVoG ๋ชจ๋ธ์€ ์‹์ƒ์˜ ์žŽ์„ ํฌํ•จํ•˜๋Š” ์ฒด์  ๋ ˆ์ด์–ด์™€ ์‹์ƒ ํ•˜๋ถ€์˜ ์ง€ํ‘œ ๋ ˆ์ด์–ด๋ฅผ ํฌํ•จํ•˜๋Š” ๋ชจ๋ธ๋กœ์„œ, ๋‘ ๋ ˆ์ด์–ด์—์„œ ๊ฒฐ์ •๋˜๋Š” ๊ฐ„์„ญ๊ธฐ๋ฒ•์˜ ์œ„์ƒ ๋ฐ ๊ธด๋ฐ€๋„๋ฅผ ์„ค๋ช…ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰ ์š”์ธ์€ ๋‘ ์˜์ƒ ์‚ฌ์ด์— ์‚ฐ๋ž€์ฒด๊ฐ€ ๋ณ€ํ™”ํ•  ๋•Œ ๋ฐœ์ƒํ•˜๋Š” ์‹œ๊ฐ„ ๋น„์ƒ๊ด€์„ฑ(temporal decorrelation)์ด๋‹ค. ํ”ฝ์…€ ์•ˆ์˜ ์‚ฐ๋ž€์ฒด๊ฐ€ ๋น„๊ท ์งˆํ•˜๊ฒŒ ์ด๋™ํ•˜๊ฑฐ๋‚˜, ์œ ์ „์ฒด์˜ ์„ฑ์งˆ์ด ๋ณ€ํ™”ํ•  ๊ฒฝ์šฐ ๋ฐœ์ƒํ•œ๋‹ค. ์ผ๋ฐ˜์ ์ธ repeat-pass ๊ฐ„์„ญ๊ธฐ๋ฒ•์˜ ๊ฒฝ์šฐ ์‹œ๊ฐ„ ๋น„์ƒ๊ด€์„ฑ์ด ๋งค์šฐ ์šฐ์„ธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์œผ๋ฉฐ, ์‹์ƒ์˜ ๊ฒฝ์šฐ ์ฒด์  ๋น„์ƒ๊ด€์„ฑ๊ณผ ์‹œ๊ฐ„ ๋น„์ƒ๊ด€์„ฑ์ด ๋™์‹œ์— ์šฐ์„ธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚œ๋‹ค. ์‹์ƒ์—์„œ ๊ด€์ฐฐ๋˜๋Š” ์ฒด์  ๋น„์ƒ๊ด€์„ฑ๊ณผ ์‹œ๊ฐ„ ๋น„์ƒ๊ด€์„ฑ์„ ๋™์‹œ์— ์„ค๋ช…ํ•˜๋Š” RMoG ๋ชจ๋ธ์ด ์ œ์•ˆ๋œ ๋ฐ” ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ƒ๋Œ€์ ์œผ๋กœ ๊ธด ์‹œ๊ฐ„ ์ฐจ์ด๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” repeat-pass ๊ฐ„์„ญ๊ธฐ๋ฒ•์—์„œ ๊ด€์ธก๋˜๋Š” ๊ธด๋ฐ€๋„ ๋ชจ๋ธ์„ ๊ณ ์•ˆํ•˜์˜€๋‹ค. ์‹œ๊ฐ„ ๋น„์ƒ๊ด€์„ฑ์„ ๋‹ค๋ฃจ๋Š” RMoG ๋ชจ๋ธ์€ ๋‘ ์˜์ƒ์˜ ์‹œ๊ฐ„ ์ฐจ์ด๊ฐ€ ํฌ์ง€ ์•Š์„ ๊ฒฝ์šฐ, ์‚ฐ๋ž€์ฒด์˜ ์ด๋™์ด ์‹œ๊ฐ„ ๋น„์ƒ๊ด€์„ฑ์„ ๋ฐœ์ƒ์‹œํ‚ค๋Š” ์ฃผ๋œ ์š”์ธ์ด๋ผ๋Š” ๊ฐ€์ •ํ•˜์— ์ œ์ž‘๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ผ๋ฐ˜์ ์ธ ์ธ๊ณต์œ„์„ฑ SAR๋Š” ์ˆ˜ ์ผ ์ด์ƒ์˜ ์‹œ๊ฐ„ ์ฐจ์ด๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ๋‹ค์ค‘ ์‹œ๊ธฐ์˜ SAR ์˜์ƒ์„ ๋‹ค๋ฃฐ ๊ฒฝ์šฐ, ๊ฐ๊ฐ์˜ ์‹œ๊ฐ„ ์ฐจ์ด๋Š” ์ƒ์ดํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚œ๋‹ค. ์ด ๊ฒฝ์šฐ ์‹œ๊ฐ„ ๋น„์ƒ๊ด€์„ฑ์„ ๋ฐœ์ƒ์‹œํ‚ค๋Š” ์š”์ธ์„ ์‚ฐ๋ž€์ฒด์˜ ์ด๋™๋งŒ์œผ๋กœ ์„ค๋ช…ํ•˜๋Š” ๊ธฐ์—๋Š” ์–ด๋ ค์›€์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ณ ์•ˆ๋œ ๋ชจ๋ธ์€ ์ง€ํ‘œ์—์„œ์˜ ๋ณ€ํ™”๋ฅผ ์‚ฐ๋ž€์ฒด์˜ ์ด๋™๊ณผ ์œ ์ „์ฒด์˜ ์„ฑ์งˆ ๋ณ€ํ™”๊ฐ€ ๊ฒฐํ•ฉ๋œ ์ƒํƒœ๋กœ ๊ฐ€์ •ํ•˜์˜€์œผ๋ฉฐ, ์‹์ƒ์˜ ์ฒด์  ๋ถ€๋ถ„์€ ์‚ฐ๋ž€์ฒด์˜ ์›€์ง์ž„์ด ์ฒด์ ์—์„œ์˜ ์‹œ๊ฐ„ ๊ธด๋ฐ€๋„๋ฅผ ๊ฐ์†Œ์‹œํ‚ค๋Š” ์ฃผ๋œ ์š”์ธ์œผ๋กœ ์ƒ๊ฐํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋‹ค์ค‘ ์‹œ๊ธฐ์˜ SAR ์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ ๊ณ„์‚ฐ๋œ ๊ธด๋ฐ€๋„๋Š” ์‹œ๊ฐ„ ์ฐจ์ด๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ๊ธด๋ฐ€๋„๊ฐ€ ๊ฐ์†Œํ•˜๋Š” ํ˜„์ƒ์„ ๊ด€์ธกํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ํŠน์ง•์€ ์‹œ๊ฐ„ ์ฐจ์ด๊ฐ€ ๊ธธ ๊ฒฝ์šฐ ๋งค์šฐ ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ์ง€๋งŒ, ์ด์ „์˜ ๋ชจ๋ธ์€ ์‹œ๊ฐ„ ์ฐจ์ด๊ฐ€ ์งง์€ ๊ฒฝ์šฐ๋ฅผ ๊ฐ€์ •ํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— ๊ทธ ์˜ํ–ฅ์ด ์ค‘์š”ํ•˜์ง€ ์•Š์•˜๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ณธ ๋ชจ๋ธ์—์„œ๋Š” ๊ธฐ์กด ๋ชจ๋ธ๊ณผ๋Š” ๋‹ค๋ฅด๊ฒŒ ๋‘ ์˜์ƒ์˜ ์‹œ๊ฐ„ ์ฐจ์ด๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ๊ธด๋ฐ€๋„๊ฐ€ ๊ฐ์†Œํ•˜๋Š” ํ˜„์ƒ์„ ์„ค๋ช…ํ•˜๊ณ ์ž ์ง€์ˆ˜ ํ˜•ํƒœ์˜ ํ•จ์ˆ˜๋ฅผ ์ง€ํ‘œ ์™€ ์ฒด์  ๋ ˆ์ด์–ด์— ๊ฐ๊ฐ ๋„์ž…ํ•˜์˜€๊ณ  ์ด๋ฅผ ์‹œ๊ฐ„ ์ข…์†์  ๊ธด๋ฐ€๋„(temporally-correlated coherence). ์ฆ‰, ์ฒด์ ๊ณผ ์ง€ํ‘œ์˜ ๋‘ ๋ ˆ์ด์–ด ์ƒ์—์„œ ๊ฐ๊ฐ์˜ ์‹œ๊ฐ„์— ๋”ฐ๋ผ์„œ ๊ฐ์†Œํ•˜๊ฒŒ ๋˜๋ฉฐ, ์ด๋Š” ํŠน์ •ํ•œ ์‹œ๊ฐ„ ์ฐจ์ด์—์„œ ๊ธด๋ฐ€๋„๊ฐ€ ํ˜•์„ฑ๋˜์—ˆ์„ ๋•Œ ํŠน๋ณ„ํ•œ ํ˜„์ƒ์ด ์—†์„ ๊ฒฝ์šฐ ์˜ˆ์ธก๋  ์ˆ˜ ์žˆ๋Š” ๊ฐ’์œผ๋กœ ์ƒ๊ฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฐ˜๋ฉด, ์˜ˆ์ธก๋˜๋Š” ๊ฐ’๊ณผ ์‹ค์ œ ๊ด€์ธก๊ฐ’๊ณผ๋Š” ์ฐจ์ด๊ฐ€ ์กด์žฌํ•˜๋ฏ€๋กœ ์ด๋Š” ์‹œ๊ฐ„ ๋…๋ฆฝ์  ๊ธด๋ฐ€๋„(temporally uncorrelated-coherence)๋กœ ํ•ด์„ํ•˜์˜€๋‹ค. ์ฒด์ ๊ณผ ์ง€ํ‘œ์˜ ์‹œ๊ฐ„ ๊ธด๋ฐ€๋„ ๊ฐ์†Œ ํ˜„์ƒ์€ ์ „์ฒด ๊ธด๋ฐ€๋„์— ์˜ํ–ฅ์„ ์ฃผ๊ธฐ ๋•Œ๋ฌธ์— ์ด๋ฅผ ์ง€ํ‘œ์™€ ์ฒด์ ์˜ ๋น„๋ฅผ ๋„์ž…ํ•˜์—ฌ, ๊ฐ๊ฐ์˜ ํšจ๊ณผ๊ฐ€ ์ „์ฒด ๊ธด๋ฐ€๋„์— ์ฃผ๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•˜์—ฌ ์ •๋Ÿ‰ํ™”ํ•˜์˜€๋‹ค. 3์žฅ์—์„œ๋Š” ์ œ์•ˆ๋œ ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋‹จ์ผ ํŽธํŒŒ์˜ ๋‹ค์ค‘ ์‹œ๊ธฐ SAR ์˜์ƒ์— ๋Œ€ํ•˜์—ฌ ๊ธด๋ฐ€๋„ ๋ณ€ํ™” ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ•ด์„์ด ๊ณ ์•ˆ๋˜์—ˆ๋‹ค. ๋ณธ ๋ฐฉ๋ฒ•์€ ์ผ๋ณธ์˜ ํ‚ค๋ฆฌ์‹œ๋งˆ ํ™”์‚ฐ์˜ 2011๋…„ ํ™”์‚ฐ ํญ๋ฐœ๋กœ ๋ฐœ์ƒํ•˜์˜€๋˜ ํ™”์‚ฐ์žฌ๋ฅผ ํƒ์ง€ ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€์œผ๋ฉฐ, ๋ณธ ๋ชฉ์ ์„ ์œ„ํ•˜์—ฌ ๋‹จ์ผ ํŽธํŒŒ์˜ ALOS PALSAR ์˜์ƒ์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. SAR ์˜์ƒ์„ ์ด์šฉํ•˜์—ฌ ์‹œ๊ฐ„ ์ฐจ์ด๊ฐ€ ๋‹ค์–‘ํ•˜๊ฒŒ ๊ธด๋ฐ€๋„๊ฐ€ ์ œ์ž‘๋˜์—ˆ๋‹ค. ์‚ฌ์šฉํ•œ multi-looking์€ 32 look์œผ๋กœ ๊ธด๋ฐ€๋„์˜ ๋ฐ”์ด์–ด์Šค๊ฐ€ ๋น„๊ต์  ์ž‘์Œ์„ ์˜๋ฏธํ•œ๋‹ค. ๋˜ํ•œ ํ”ฝ์…€์˜ ๋Œ€๋ถ€๋ถ„์—์„œ์˜ ์—ด์  ๋น„์ƒ๊ด€์„ฑ(thermal decorrelation)์€ ๋ฌด์‹œํ•  ์ˆ˜ ์žˆ์„ ์ •๋„๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ๊ธฐํ•˜ํ•™์  ๋น„์ƒ๊ด€์„ฑ(geometric decorrelation)์€ common-wave spectral filtering์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐ์†Œ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ๋Œ€์ƒ ํ™”์‚ฐ์€ ์‹์ƒ์ด ๋ถ„ํฌํ•˜๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์ฒด์  ๋น„์ƒ๊ด€์„ฑ(volume decorrelation)์„ ์ตœ์†Œํ™”ํ•˜์—ฌ์•ผ ํ•  ํ•„์š”์„ฑ์ด ์žˆ๋‹ค. ์ฒด์  ๋น„์ƒ๊ด€์„ฑ์€ ์‹์ƒ์˜ ๋†’์ด, ์‹์ƒ์˜ ์ˆ˜์ง์ ์ธ ๊ตฌ์กฐ, ๋‘ ๋ ˆ์ด๋” ์„ผ์„œ์˜ ๊ธฐ์„ ๊ฑฐ๋ฆฌ(spatial baseline)๋“ฑ์— ์˜ํ•˜์—ฌ ๊ฒฐ์ •๋œ๋‹ค. ์‹์ƒ์˜ ๋ฌผ๋ฆฌ์ ์ธ ํŒŒ๋ผ๋ฏธํ„ฐ๋Š” ์—ฐ๊ตฌ์—์„œ ์ˆ˜์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๋ณ€์ˆ˜๊ฐ€ ์•„๋‹Œ ๋ฐ˜๋ฉด, ๋‹ค์ค‘ ์‹œ๊ธฐ์—์„œ ๋งŒ๋“ค์–ด ์ง„ ์˜์ƒ์€ ๋‹ค์ˆ˜์˜ ๊ธฐ์„ ๊ฑฐ๋ฆฌ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ธฐ์„ ๊ฑฐ๋ฆฌ์— ๋Œ€ํ•œ ์กฐ๊ฑด์ด ์„ค์ •ํ•จ์œผ๋กœ์จ ์ฒด์  ๋น„์ƒ๊ด€์„ฑ์„ ์ตœ์†Œํ™” ํ•  ์ˆ˜ ์žˆ๋‹ค. RVoG ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ณ„์‚ฐ๋œ ๊ฒฐ๊ณผ ALOS PALSAR์˜ ๊ฒฝ์šฐ ์•ฝ 1000m์˜ ๊ธฐ์„ ๊ฑฐ๋ฆฌ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์„ ๋•Œ ์ฒด์  ๊ธด๋ฐ€๋„๋Š” ์•ฝ 0.94 ์ด์ƒ์ด ๋จ์„ ์•Œ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋Š” ์ฒด์  ๊ธด๋ฐ€๋„๋ฅผ ๊ณ ๋ คํ•˜์ง€ ์•Š์•„๋„ ๋จ์„ ์˜๋ฏธํ•œ๋‹ค. ์•ž์„œ 2์žฅ์—์„œ ์ œ์•ˆ๋œ ๊ธด๋ฐ€๋„ ๋ชจ๋ธ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์ถ”์ถœ์„ ์œ„ํ•˜์—ฌ ์ž๋ฃŒ๋Š” ํ™”์‚ฐ ํญ๋ฐœ ์ „์˜ ๊ฐ„์„ญ์Œ๊ณผ ํ™”์‚ฐํญ๋ฐœ ์ „ํ›„์˜ ๊ฐ„์„ญ์Œ์˜ ๋‘ ๊ทธ๋ฃน์œผ๋กœ ๋‚˜๋ˆ„์–ด์กŒ๋‹ค. ์šฐ์„  ํ™”์‚ฐ ํญ๋ฐœ ์ด์ „์˜ ๊ธด๋ฐ€๋„์— ๋Œ€ํ•œ ํ•ด์„ ๋ฐ ์ดํ•ด๋ฅผ ์œ„ํ•˜์—ฌ ๊ธด๋ฐ€๋„ ๋ชจ๋ธ์ด ์ ์šฉ๋˜์—ˆ๋‹ค. ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ์—์„œ ์ค‘์š”ํ•œ ๊ฒƒ์€ ๋ชจ๋ธ์— ํฌํ•จ๋˜์–ด ์žˆ๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์ˆ˜์™€ ๊ด€์ธก ๊ฐ’์˜ ์ˆ˜๋กœ, ๊ด€์ธก๊ฐ’์ด ์ถฉ๋ถ„ํ•  ๊ฒฝ์šฐ์—๋งŒ ์ •ํ™•ํ•œ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ถœ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋‹จ์ผ ํŽธํŒŒ์˜ ๋‹ค์ค‘ ์‹œ๊ธฐ ์˜์ƒ์„ ๋‹ค๋ฃจ๋Š” ๊ฒฝ์šฐ ๋ฏธ์ง€์ˆ˜์˜ ๊ฐœ์ˆ˜๊ฐ€ ๋” ๋งŽ๊ธฐ ๋•Œ๋ฌธ์— ์ •ํ™•ํ•œ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ถœ์€ ์–ด๋ ค์šธ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ชจ๋ธ์˜ ํŠน์„ฑ์„ ์ด์šฉํ•œ ๊ฐ€์ •์„ ๋ฐ”ํƒ•์œผ๋กœ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์ถ”์ถœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ถœ์˜ ์ฒซ ๋ฒˆ์งธ๋Š” ์ง€ํ‘œ๋Œ€ ์ฒด์ ๋น„ ๋ฐ ์‹œ๊ฐ„ ์ข…์†์  ๊ธด๋ฐ€๋„์˜ ์ถ”์ •์œผ๋กœ ์ด๋Š” ๋‘ ์ง€์ˆ˜ ํ˜•ํƒœ์˜ ๊ณก์„  ์ ํ•ฉ(curve fitting)์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ๊ฒฐ๊ณผ๋กœ๋ถ€ํ„ฐ ์ถ”์ถœ๋œ ๊ฐ ํ”ฝ์…€์˜ ํŠน์ง•์  ์‹œ๊ฐ„ ์ƒ์ˆ˜(characteristic time constant)๋Š” ๊ทธ ํ”ฝ์…€์ด ์‹œ๊ฐ„์˜ ๋ณ€ํ™”์— ๋”ฐ๋ผ ๊ธด๋ฐ€๋„์˜ ์•ˆ์ •์„ฑ์„ ๋ณด์ด๋Š” ์ƒ์ˆ˜๋กœ, ๋†’์„์ˆ˜๋ก ๊ธด ์‹œ๊ฐ„ ์ฐจ์ด์—๋„ ๊ธด๋ฐ€๋„๊ฐ€ ๋†’์Œ์„ ์˜๋ฏธํ•œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ์ธ๊ณต์ ์ธ ๊ตฌ์กฐ๋ฌผ์ด๋‚˜, ์‹์ƒ์ด ์—†๋Š” ๋‚˜์ง€(bare soil)์—์„œ ๋†’์€ ๊ฐ’์„ ๋ณด์ž„์„ ์•Œ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋ฐ˜๋ฉด ์‹์ƒ์ด ์žˆ๋Š” ํ”ฝ์…€์€ ์ƒ๋Œ€์ ์œผ๋กœ ๋‚ฎ์€ ๊ฐ’์„ ๋ณด์˜€๋‹ค. ์ถ”์ •๋œ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‹œ๊ฐ„ ๋…๋ฆฝ์  ๊ธด๋ฐ€๋„๋ฅผ ์ถ”์ •ํ•˜์˜€์œผ๋‚˜, ์ด ๋•Œ ๋ฏธ์ง€์ˆ˜๊ฐ€ ๊ด€์ธก ๊ฐ’์˜ ๊ฐœ์ˆ˜๋ณด๋‹ค ๋งŽ์œผ๋ฏ€๋กœ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •์— ๋ถˆํ™•์‹ค์„ฑ์ด ์กด์žฌํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ง€ํ‘œ์™€ ์ฒด์ ์—์„œ์˜ ์‹œ๊ฐ„ ์ข…์†์  ๊ธด๋ฐ€๋„์˜ ๋น„๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฐ ํ”ฝ์…€ ๋ฐ ๊ฐ ์‹œ๊ฐ„์ฐจ์ด๋ฅผ ๊ฐ–๋Š” ๊ธด๋ฐ€๋„์—์„œ ์ฒด์ ๊ณผ ์ง€ํ‘œ์˜ ์‹œ๊ฐ„ ๋น„์ƒ๊ด€์„ฑ ์ค‘ ์šฐ์„ธํ•œ ํ˜„์ƒ์„ ํƒ์ง€ํ•˜์—ฌ ์šฐ์„ธํ•˜์ง€ ์•Š์€ ํ˜„์ƒ์„ ๋ฌด์‹œํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ๊ฐ€์ •ํ•˜์˜€๋‹ค. ์ฆ‰, ๋งŒ์•ฝ ์ง€ํ‘œ์˜ ์‹œ๊ฐ„ ์ข…์†์  ๊ธด๋ฐ€๋„๊ฐ€ ์ฒด์ ์˜ ์‹œ๊ฐ„ ์ข…์†์  ๊ธด๋ฐ€๋„๋ณด๋‹ค ๊ทธ ํšจ๊ณผ๊ฐ€ ํฌ๋‹ค๋ฉด, ์‹œ๊ฐ„ ๋…๋ฆฝ์  ๊ธด๋ฐ€๋„๊ฐ€ ์ฃผ๋กœ ์ง€ํ‘œ๋กœ๋ถ€ํ„ฐ ๊ธฐ์ธ๋œ๋‹ค๊ณ  ๊ฐ€์ •ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ์‹์ƒ์˜ ๊ธด๋ฐ€๋„๋Š” ์ง€ํ‘œ์˜ ๊ธด๋ฐ€๋„์™€ ์ฒด์ ์˜ ๊ธด๋ฐ€๋„์˜ ์˜ํ–ฅ์ด ๋ณตํ•ฉ์ ์œผ๋กœ ์ž‘์šฉํ•˜์—ฌ ๊ฒฐ์ •๋œ๋‹ค. ์ด ๋•Œ ์ฒด์ ์˜ ๊ธด๋ฐ€๋„์˜ ๋ฐ”๋žŒ์— ์˜ํ•˜์—ฌ์„œ๋„ ์‰ฝ๊ฒŒ ๋ณ€ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ผ ๊ทธ ์˜ํ–ฅ์ด ๊ฑฐ์˜ ๋ฌด์‹œํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์‹œ๊ฐ„ ์ฐจ์ด๊ฐ€ ์งง์„ ๊ฒฝ์šฐ ์‹์ƒ์ด ๊ธด๋ฐ€๋„์— ์ฃผ๋„์ ์œผ๋กœ ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ์ง€๋งŒ, ์‹œ๊ฐ„ ์ฐจ์ด๊ฐ€ ๊ธด ๊ฒฝ์šฐ ์ง€ํ‘œ๊ฐ€ ์šฐ์„ธํ•˜๊ฒŒ ๊ธด๋ฐ€๋„์— ์˜ํ–ฅ์„ ์ค€๋‹ค. ์ด์™€ ๊ฐ™์€ ๊ฐ€์ •์„ ํ†ตํ•˜์—ฌ ์‹œ๊ฐ„ ๋…๋ฆฝ์  ๊ธด๋ฐ€๋„๋ฅผ ์ถ”์ถœํ•˜์˜€๋‹ค. ๊ฐ ํ”ฝ์…€์—์„œ ๊ด€์ฐฐ๋˜๋Š” ๊ธด๋ฐ€๋„์˜ ํ˜„์ƒ์„ ํ†ต๊ณ„์ ์œผ๋กœ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ž์—ฐ ์žฌํ•ด๊ฐ€ ํฌํ•จ๋˜์ง€ ์•Š์€ ์ž๋ฃŒ์˜ ์‹œ๊ฐ„ ์ข…์†์  ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ํžˆ์Šคํ† ๊ทธ๋žจ์„ ์ œ์ž‘ํ•˜์˜€๊ณ , ์ด๋ฅผ ๊ธฐ๋ฐ˜์˜ ์ž์—ฐ ์žฌํ•ด๊ฐ€ ๊ธฐ์กด์— ๋ฐœ์ƒํ•˜์˜€๋˜ ์ž์—ฐ ํ˜„์ƒ์ด ๊ฐ€๋Šฅ์„ฑ์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๋ฐ˜๋Œ€๋กœ ์ด ์ˆ˜์น˜๋Š” ์ž์—ฐ ํ˜„์ƒ์ด ์•„๋‹ ํ™•๋ฅ ์„ ์˜๋ฏธํ•˜๊ธฐ๋„ ํ•œ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ALOS ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ™”์‚ฐ์žฌ๊ฐ€ ์Œ“์—ฌ์žˆ์„ ํ™•๋ฅ ๋„๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ์˜ ๊ฒ€์ฆ์€ ์‹ค์ œ ํ˜„์žฅ ์กฐ์‚ฌ๋ฅผ ํ†ตํ•˜์—ฌ ํš๋“๋œ ํ™”์‚ฐ์žฌ์˜ ๋‘๊ป˜์™€ ์˜์—ญ ๋ฐ€๋„ (area density)์™€์˜ ๋น„๊ต๋ฅผ ํ†ตํ•˜์—ฌ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๊ฒ€์ฆ ๊ฒฐ๊ณผ๋Š” ๋‘๊ป˜๋กœ ์•ฝ 5 cm ์ด์ƒ, ์˜์—ญ ๋ฐ€๋„๋กœ ์•ฝ 10 kg/m2 ์ด์ƒ์˜ ํ™”์‚ฐ์žฌ๊ฐ€ ์Œ“์ธ ์ง€์—ญ์—์„œ ์ƒ๊ด€์„ฑ์„ ๋ณด์ž„์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์„ฑ๊ณต์ ์œผ๋กœ ์žฌํ•ด์— ๋Œ€ํ•œ ๋ณ€ํ™”๋ฅผ ํƒ์ง€ํ•˜์˜€์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. 4์žฅ์—์„œ๋Š” ๊ธด๋ฐ€๋„ ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ๋‹ค์ค‘ ์‹œ๊ธฐ์˜ ๋‹ค์ค‘ ํŽธํŒŒ SAR ์˜์ƒ์„ ํ™œ์šฉํ•˜์—ฌ ์ž์—ฐ ์žฌํ•ด ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์ ์šฉ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•˜์—ฌ 2009๋…„๋ถ€ํ„ฐ 2015๋…„๊นŒ์ง€์˜ 15์žฅ์˜ UAVSAR ์ž๋ฃŒ๊ฐ€ ํ™œ์šฉ๋˜์—ˆ์œผ๋ฉฐ, ๋ฏธ๊ตญ ์บ˜๋ฆฌํฌ๋‹ˆ์•„ ์ฃผ์—์„œ ๋ฐœ์ƒํ•œ 2015๋…„์˜ ์‚ฐ๋ถˆ ์ค‘ ํ•˜๋‚˜์ธ Lake fire์— ๋Œ€ํ•˜์—ฌ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๊ธด๋ฐ€๋„ ์˜์ƒ์—์„œ ์‚ฐ๋ถˆ์— ์˜ํ•œ ๊ธด๋ฐ€๋„ ๊ฐ์†Œ ํ˜„์ƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ์ง€๋งŒ, ์‹์ƒ ์ง€์—ญ์˜ ์ž์—ฐ ํ˜„์ƒ์— ์˜ํ•œ ๊ธด๋ฐ€๋„ ๊ฐ์†Œ ํ˜„์ƒ๊ณผ ๋ณตํ•ฉ์ ์œผ๋กœ ๋ฐœ์ƒํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— ํ•ด์„์— ์–ด๋ ค์›€์ด ์žˆ์—ˆ๋‹ค. ์˜์ƒ์˜ ์ง„ํญ ์˜์ƒ์„ ์ด์šฉํ•œ ์ž์—ฐ ์žฌํ•ด ํƒ์ง€์—๋„ ์‚ฐ๋ถˆ ํƒ์ง€ํ•  ๋งŒํผ ๋ฏผ๊ฐ๋„๊ฐ€ ์ถฉ๋ถ„ํ•˜์ง€ ์•Š์•˜๋‹ค. 3์žฅ๊ณผ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ๋ณธ ์—ฐ๊ตฌ ์ง€์—ญ์—์„œ ๊ธด๋ฐ€๋„๋‚˜ ์ง„ํญ๋งŒ์„ ์‚ฌ์šฉํ•ด์„œ๋Š” ์ •ํ™•ํ•œ ํ”ผํ•ด ์ง€๋„๋ฅผ ๋งŒ๋“ค๊ธฐ ์–ด๋ ค์› ์œผ๋ฉฐ, ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๊ธด๋ฐ€๋„ ๋ชจ๋ธ์„ ์ ์šฉํ•œ ํ”ผํ•ด ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•  ํ•„์š”์„ฑ์ด ์žˆ์—ˆ๋‹ค. 3์žฅ์—์„œ ์ œ์•ˆ๋œ ๋ชจ๋ธ ํ•ด์„ ๋ฐฉ๋ฒ•๊ณผ๋Š” ์ฐจ์ด์ ์ด ์žˆ๋Š”๋ฐ, ๊ทธ๊ฒƒ์ธ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์‚ฌ์šฉ๋˜๋Š” UAVSAR ์ž๋ฃŒ๊ฐ€ ๋‹ค์ค‘ ํŽธํŒŒ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ๊ณต๊ฐ„ ๊ธฐ์„  ๊ฑฐ๋ฆฌ๊ฐ€ ๊ฑฐ์˜ 0์— ๊ฐ€๊น๋‹ค๋Š” ํŠน์ง•์ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋‹จ์ผ ํŽธํŒŒ ์ž๋ฃŒ์—์„œ๋Š” ๋งค๊ฐœ ๋ณ€์ˆ˜์˜ ๊ฐ’์ด ๊ด€์ธก๊ฐ’๋ณด๋‹ค ๋งŽ์•˜์ง€๋งŒ, ๋‹ค์ค‘ ํŽธํŒŒ์˜ ๊ฒฝ์šฐ ๊ด€์ธก๊ฐ’์ด ๋” ๋งŽ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •์— ํ•„์š”ํ–ˆ๋˜ ๊ฐ€์ •์„ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. ๋˜ํ•œ ๊ณต๊ฐ„ ๊ธฐ์„ ๊ฑฐ๋ฆฌ๊ฐ€ ๊ฑฐ์˜ 0์— ๊ฐ€๊น๋‹ค๋Š” ๊ฒƒ๋„ ์ฒด์  ๋น„์ƒ๊ด€์„ฑ์„ ๋ฌด์‹œํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๊ด€์ธก๋œ ๊ธด๋ฐ€๋„๋Š” ๊ฑฐ์˜ ์‹œ๊ฐ„ ๋น„์ƒ๊ด€์„ฑ๊ณผ ๊ด€๋ จ ์žˆ๋‹ค๊ณ  ์ƒ๊ฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์ถ”์ถœํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์€ ํฌ๊ฒŒ 3๊ฐ€์ง€๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ๋Š” ์ง€ํ‘œ์™€ ์ฒด์ ์— ๋Œ€ํ•œ ๊ธด๋ฐ€๋„ ์˜ํ–ฅ์„ ๋ถ„๋ฆฌํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์šฐ์„ ์ ์œผ๋กœ ๊ธด๋ฐ€๋„ ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹ค์ค‘ ์‹œ๊ธฐ ์˜์ƒ๋งˆ๋‹ค ๋‹ค๋ฅธ ์ตœ์ ํ™” ๋ฒกํ„ฐ๋ฅผ ์ƒ์ •ํ•˜๋Š” MSM ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜์˜€๋‹ค. ์ด ๊ณผ์ •์„ ํ†ตํ•˜์—ฌ ๊ด€์ธกํ•  ์ˆ˜ ์žˆ๋Š” ๊ธด๋ฐ€๋„๊ฐ€ ์ตœ๋Œ€์น˜๊ฐ€ ๋˜๊ฒŒ ๋งŒ๋“œ๋Š” ํŽธํŒŒ์™€ ๊ทธ์™€ ์ˆ˜์งํ•˜๋Š” ํŽธํŒŒ๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋ชจ๋ธ ํ•ด์„๊ณผ ์—ฐ๊ด€์‹œ์ผฐ์„ ๋•Œ ์ตœ๋Œ€์น˜๊ฐ€ ๋˜๋Š” ๊ธด๋ฐ€๋„๋Š” ์ง€ํ‘œ์˜ ๋ณ€ํ™”์—, ์ตœ์†Œํ™”๋˜๋Š” ๊ธด๋ฐ€๋„๋Š” ์ฒด์ ์˜ ๋ณ€ํ™”์™€ ๊ด€๋ จ๋˜์–ด ์žˆ๋‹ค๊ณ  ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋‹จ๊ณ„์—์„œ๋Š” ์‹œ๊ฐ„ ์ข…์†์  ๊ธด๋ฐ€๋„์— ํ•ด๋‹นํ•˜๋Š” ๋ณ€์ˆ˜์ธ ํŠน์ง•์  ์‹œ๊ฐ„ ์ƒ์ˆ˜๋ฅผ ์ถ”์ถœํ•˜์˜€์œผ๋ฉฐ, ์ง€ํ‘œ๋Œ€ ์ฒด์ ๋น„ ์—ญ์‹œ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๋‹จ์ผ ํŽธํŒŒ ์ถ”์ • ๋ฐฉ๋ฒ•๊ณผ ๋‹ค๋ฅด๊ฒŒ ๋‹ค์ค‘ ํŽธํŒŒ ์˜์ƒ์—์„œ๋Š” ๋ชจ๋“  ํŽธํŒŒ์˜ ๊ธด๋ฐ€๋„๋ฅผ ์ด์šฉํ•˜์—ฌ ์ฒด์ ๊ณผ ์ง€ํ‘œ์—์„œ์˜ ์‹œ๊ฐ„ ์ข…์†์  ๊ธด๋ฐ€๋„๋ฅผ ์ถ”์ •ํ•œ๋‹ค. ์„ธ๋ฒˆ์งธ ๋‹จ๊ณ„์—์„œ๋Š” ์ฒด์ ๊ณผ ์ง€ํ‘œ์—์„œ์˜ ์‹œ๊ฐ„ ๋…๋ฆฝ์  ๊ธด๋ฐ€๋„๋ฅผ ๋™์‹œ์— ์ถ”์ •ํ•˜๋ฉฐ 3์žฅ๊ณผ๋Š” ๋‹ค๋ฅธ ๊ฒƒ์€ ์ด ๊ณผ์ •์—์„œ ๊ฐ€์ •์ด ํ•„์š”ํ•˜์ง€ ์•Š๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋ณธ ๊ณผ์ •์„ ํ†ตํ•˜์—ฌ ์ถ”์ •๋œ ํŒŒ๋ผ๋ฏธํ„ฐ ์ค‘ ์‹œ๊ฐ„ ๋…๋ฆฝ์  ๊ธด๋ฐ€๋„๋Š” ์‹œ๊ฐ„ ์ข…์†์  ๊ธด๋ฐ€๋„๋กœ๋ถ€ํ„ฐ ์„ค๋ช…๋˜์ง€ ์•Š๋Š” ๋ถ€๋ถ„์„ ์ถ”๊ฐ€์ ์œผ๋กœ ์„ค๋ช…ํ•˜๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ๋กœ์จ ๊ฐ‘์ž‘์Šค๋Ÿฝ๊ฒŒ ์ผ์–ด๋‚˜๋Š” ๋ณ€ํ™”๋ฅผ ์˜๋ฏธํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฐ ํ”ฝ์…€์—์„œ ๊ณผ๊ฑฐ ๋™์•ˆ ๋ฐœ์ƒํ•˜์˜€๋˜ ์ž์—ฐ ํ˜„์ƒ์ด ๊ธด๋ฐ€๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์‚ฐ๋ถˆ์€ ๋น„๊ต์  ๊ฐ•ํ•œ ๊ธด๋ฐ€๋„ ๊ฐ์†Œ๋ฅผ ๋ฐœ์ƒ์‹œํ‚ค๊ธฐ ๋•Œ๋ฌธ์— ํ†ต๊ณ„์ ์ธ ์ ‘๊ทผ์„ ํ†ตํ•˜์—ฌ ํ™•๋ฅ ์ ์ธ ํ”ผํ•ด ๊ฐ€๋Šฅ์„ฑ์„ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์‚ฐ๋ถˆ์˜ ๊ฒฝ๊ณ„ ๋ถ€๋ถ„์˜ ์ž๋ฃŒ์™€์˜ ์ƒ๋Œ€์ ์ธ ๋น„๊ต๋ฅผ ํ†ตํ•œ ๊ฒ€์ฆ ๊ฒฐ๊ณผ์„ ํ†ตํ•˜์—ฌ ๊ธด๋ฐ€๋„๋งŒ์„ ์ด์šฉํ•˜์—ฌ ํ”ผํ•ด ์ง€์—ญ์„ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ณด๋‹ค ์˜คํƒ์ง€๋ฅ ์„ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. 4์žฅ์—์„œ ์‚ฌ์šฉ๋œ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ • ๊ฒฐ๊ณผ์˜ ๊ฒ€์ฆ์„ ์œ„ํ•˜์—ฌ ์ด์ „์˜ ๊ฒ€์ฆ์ด ์ง„ํ–‰๋˜์–ด ์™”๋˜ RMoG ๋ชจ๋ธ๊ณผ ์ƒ๋Œ€ ๋น„๊ต๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. RMoG์˜ ์ฒด์ ๊ณผ ์ง€ํ‘œ ๋ถ€๋ถ„์˜ ์‹œ๊ฐ„ ๋น„์ƒ๊ด€์„ฑ ํ•จ์ˆ˜๋Š” ๋ณธ ์—ฐ๊ตฌ์—์„œ ์‚ฌ์šฉ๋œ ๋ชจ๋ธ์˜ ์‹œ๊ฐ„ ์ข…์†์  ๊ธด๋ฐ€๋„์™€ ์‹œ๊ฐ„ ๋…๋ฆฝ์  ๊ธด๋ฐ€๋„์˜ ๊ณฑ์œผ๋กœ ํ‘œํ˜„๋  ์ˆ˜ ์žˆ๋‹ค. ๋น„๊ตํ•œ ๊ฒฐ๊ณผ๋Š” ๋†’์€ ์ƒ๊ด€์„ฑ์„ ๋ณด์ด๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ๋‹จ์ผ ํŽธํŒŒ์™€ ๋‹ค์ค‘ ํŽธํŒŒ๋ฅผ ์‚ฌ์šฉํ•œ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ • ๊ฒฐ๊ณผ์™€ ์žฌํ•ด ํƒ์ง€ ๊ฒฐ๊ณผ๋„ ๋น„๊ตํ•˜์˜€๋‹ค. ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •์˜ ๊ฒฝ์šฐ, ๋‹จ์ผ ํŽธํŒŒ์—์„œ ์ถ”์ •๋œ ๊ฒฐ๊ณผ๊ฐ€ ๋‹ค์†Œ ์ž‘์Œ์ด ํ™•์ธ๋˜์—ˆ์œผ๋ฉฐ, ์ด๊ฒƒ์€ ๋‹จ์ผ ํŽธํŒŒ(HH)๊ฐ€ ์ง€ํ‘œ์™€ ์ฒด์  ์‚ฌ์ด์˜ ์‚ฐ๋ž€ ์ค‘์‹ฌ์—์„œ ๊ธฐ๋ก๋œ ๊ฒƒ์œผ๋กœ ๊ทธ ์›์ธ์„ ์ถ”์ •ํ•ด๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ํ”ผํ•ดํƒ์ง€ ๋ฐฉ๋ฒ•์—์„œ์˜ ์ •ํ™•๋„๋Š” ๋‹ค์ค‘ ํŽธํŒŒ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์— ์šฐ์„ธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์ง€๋งŒ, ๊ฑฐ์˜ ์œ ์‚ฌํ•œ ์ •๋„์˜ ์ •ํ™•๋„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆ๋œ ํ”ผํ•ด ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ž์—ฐ ํ˜„์ƒ์—์„œ ๋น„๋กฏ๋˜๋Š” ๊ธด๋ฐ€๋„ ๊ฐ์†Œ ํ˜„์ƒ์„ ๋ถ„์„ํ•˜์—ฌ ์ž์—ฐ ์žฌํ•ด๋กœ๋ถ€ํ„ฐ ๋ฐœ์ƒํ•˜๋Š” ํ˜„์ƒ์„ ๊ตฌ๋ณ„ํ•˜์—ฌ ํ”ผํ•ด๋กœ ๊ทœ์ •ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด, ๊ธฐ์กด์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋ณด๋‹ค ์ •ํ™•๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ๋‹ค์ค‘ ํŽธํŒŒ ๊ฐ„์„ญ๊ณ„ SAR ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ, ๋‹ค์ค‘ ํŽธํŒŒ์— ๊ธฐ๋ก๋˜์–ด ์žˆ๋Š” ๋‹ค๋ฅธ ์‚ฐ๋ž€ ์ค‘์‹ฌ์—์„œ์˜ ๋ณ€ํ™”๋ฅผ ์ด์šฉํ•˜์—ฌ ์ฒด์  ๋ฐ ์ง€ํ‘œ์—์„œ์˜ ๋ณ€ํ™”๋ฅผ ๋…๋ฆฝ์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜์—ฌ ํ”ผํ•ด๋ฅผ ํƒ์ง€ํ•˜์˜€๋‹ค. ์ด์™€ ๊ฐ™์€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋‹ค์ˆ˜์˜ ์ž์—ฐ ์žฌํ•ด์— ์ ์šฉ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๊ฐ ํ”ฝ์…€์˜ ๊ธด๋ฐ€๋„ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋‹ค์–‘ํ•œ ์ง€ํ‘œ ํƒ€์ž…์— ์ ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ๋˜ํ•œ ๋ฌผ๋ฆฌ์ ์ธ ํ•ด์„์„ ๋ณ‘ํ•ฉํ•˜์—ฌ ํ”ผํ•ด์˜ ์‹ฌ๊ฐ๋„๋ฅผ ์ •๋Ÿ‰ํ™” ํ•  ์ˆ˜ ์žˆ์€ ๊ฐ€๋Šฅ์„ฑ ์—ญ์‹œ ์กด์žฌ ํ•˜๋ฉฐ, ํ–ฅํ›„ ๋ฐœ์‚ฌ๋  ์ธ๊ณต์œ„์„ฑ์˜ ๋ฏธ์…˜์—์„œ๋„ ์ ์šฉ๋  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋ณธ ์—ฐ๊ตฌ์˜ ์˜์˜๊ฐ€ ํฌ๋‹ค๊ณ  ํŒ๋‹จํ•  ์ˆ˜ ์žˆ๋‹ค.For rapid response and efficient recovery, the accurate assessment of damaged area caused by the natural disaster is essential. SAR system has been known as a powerful and effective tool for estimating damaged area due to its imaging capability at night and cloudy days. One of the damage assessment methods is based on interferometric coherence generated from two or more SAR images, namely coherent change detection. The interferometric coherence is a very sensitive detector to subtle changes induced by dielectric properties and positional disturbance of scatterers. However, the conventional approaches using the interferometric coherence have several limitations in understanding the damage mechanism caused by natural disasters and providing the accurate spatial information. These limitations come from the complicated mechanism determining the coherence. A number of sources including the sensor geometry, radar parameters, and surface conditions can induce the decorrelation. In particular, the interpretation complexity of the interferometric coherence is severe over the vegetated area, due to the volumetric decorrelation and temporal decorrelation. It is a remaining problem that the decorrelation caused by the natural phenomena such as the wind, rain, and snow can come along the decorrelation caused by natural disaster. Therefore, a new accurate approach needs to be designed in order to interpret the decorrelation sources and discriminate the effect of natural disaster from that of natural phenomena. This research starts from the development of the temporal decorrelation model to interpret the interferometric coherence observed in multi-temporal SAR data. Then, the coherence model is extended to be applied to the damage mapping algorithm for single- and fully-polarimetric SAR data for detecting the damaged area caused by volcanic ash and wildfire. The coherence model is designed so that it explains the coherence behavior observed in the multi-temporal SAR data. The noticeable characteristic is that the interferometric coherence tends to decrease as the time-interval increases. Also, the coherence for multi-layer is determined by the different contributions of each layer. For example, the volume and ground layer can affect the total coherence observed in the forest area. In order to reflect the realistic condition and physically interpret the coherence, the coherence model proposed in this research includes several decorrelation sources such as temporally correlated dielectric changes, temporally uncorrelated dielectric changes and the motions in the two layersi.e. ground and volume layer. According to the proposed model, the coherent behavior of each layer is explained by exponentially decreasing coherence (temporally-correlated coherence), and the difference between the observed coherence and the temporally-correlated coherence is interpreted as the temporally-uncorrelated coherence. The ground-to-volume ratio plays an important role to determine the contributions of temporal decorrelations in ground and volume layer. Suggested model is applied into the coherent change detection for multi-temporal and single-polarized SAR data. The method is evaluated for detection of volcanic ash emitted from Kirishima volcano in 2011 using ALOS PALSAR data. The criterion of the spatial baseline is calculated based on the Random Volume over Ground model to minimize the volumetric decorrelation. The model parameters are extracted under the several assumptions, and then the historical coherence behavior is analyzed using kernel density estimation method. By comparing the changes of model parameters between the reference pairs and event pairs, the probability of surface changes caused by volcanic ash is defined. The in-situ data, which measure the depth and area density of volcanic ash, is compared with the calculated probability maps for determining the threshold and evaluating the performance. The correlation is found over the area where the depth of the volcanic ash is more than 5 cm and the area density is more than 10 kg/m2. The temporal decorrelation model is also used for change detection using multi-temporal and fully-polarimetric interferometric SAR data. By introducing polarimetric and interferometric SAR data, the assumptions used in the method for single-polarized SAR data are reduced and the changes of two layer can be estimated separately. The approach is applied to detect the burnt area caused by the Lake fire, in June 2015 using UAVSAR data. Even though, coherence analysis shows the loss of coherence due to the fire event, the temporal decorrelation caused by the natural changes is mixed with the signal of the event. In order to apply the coherence model and extract the model parameter, here, the three steps are proposedcoherence optimization, temporally-correlated coherence estimation, and temporally-uncorrelated coherence estimation. Then, the extracted model parameters are used for the damage assessment using the probability determination based on the history of natural phenomena. The final generated damage map shows higher performance than the damage mapping method using coherence only. Also, the comparison result with the RMoG model shows high agreement, which implies the extraction of the model parameters is reliable. One of the advantages of the proposed algorithm is that the more accurate delineation of damage area can be expected by isolating the decorrelation caused by the natural disaster from the effect of natural phenomena. Moreover, a distinguishable benefit can be obtained that the changes over ground and volume layers can be assessed separately by utilizing the multi-temporal full-polarimetric SAR data.Chapter 1. Introduction 1 1.1. Brief overview of SAR and its applications 1 1.2. Motivations 5 1.3. Purpose of Research 8 1.4. Outline 10 Chapter 2. Estimation of complex correlation and decorrelation sources 11 2.1. Estimation of complex correlation 11 2.2. Decorrelation sources 14 2.3. Derivation of coherence model assuming two layers for repeat-pass interferometry 35 Chapter 3. Damage mapping using temporal decorrelation model for single-polarized SAR data : A case study for volcanic ash 51 3.1. Description of study area 51 3.2. Data description 55 3.3. Extraction of temporal decorrelation parameters 61 3.4. Probability map generation 68 3.5. Mapping volcanic ash 73 3.6. Discussion 76 Chapter 4.Damage mapping using temporal decorrelation model for multi-temporal and fully-polarized SAR data 78 4.1. Description of Lake Fire and UAVSAR data 79 4.2. Brief analysis of SAR amplitude and interferometric coherence 82 4.3. Damage mapping algorithm using coherence model 89 4.4. Applicable conditions of damage mapping algorithm using coherence model 114 4. 5. Comparison of model inversion results and damage mapping algorithm results 120 4. 6. Discussion and conclusion 129 Chapter 5. Conclusions and Future Perspectives 132 Abstract in Korean 140 Bibliography 147Docto
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