다년도 분광 데이터를 이용한 콩의 생체중, 엽면적 지수 추정 (Estimation of Fresh Weight and Leaf Area Index of Soybean (Glycine max) Using Multi-year Spectral Data)
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- 한국농림기상학회지
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- 제23권4호
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- pp.329-339
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- 2021
콩은 논 대표적인 밭작물로써 온도, 수분, 토양과 같은 환경 조건에 민감하기 때문에 재배 시 포장 관리가 매우 중요하다. 작물 상태를 비파괴적, 비접촉적 방법으로 측정할 수 있는 분광 기술을 활용한다면 작황 예측, 작물 스트레스 및 병충해 판별 등 생육 진단 및 처방을 통해 품질과 수확량을 높일 수 있다. 본 연구에서는 회전익 무인기에 탑재된 다중분광 센서를 이용하여 시험 포장에서 콩 생육 추정 모델 개발하고 재현성을 확인하기 위해 농가 포장에 검증을 수행하였다. 분광 데이터로 산출된 정규화 식생지수(NDVI, GNDVI), 단순비 식생지수(RRVI, GRVI)와 콩 생육 데이터(생체중, LAI)를 선형회귀분석을 실시하여 모델을 개발하였으며 괴산에 위치한 농가포장에서 검증을 실시하였다. 그 결과 생체중의 경우 정규화 식생지수를 이용 시 포화되기 때문에 단순비 식생지수 GRVI를 이용한 모델의 성능이 가장 높았다(R2=0.74, RMSE=246 g/m2, RE=34.2%). 괴산 농가 포장에 생체중 모델 검증 결과 RMSE=392 g/m2, RE=32%로 나타났으며 작부 체계별 나누어 검증 결과 단작 포장과 이모작 포장 생체중 모델은 RMSE=315 g/m2, RE=26% 및 RMSE=381 g/m2, RE=31%로 나타났다. 작부 체계별 포장과 적산온도가 유사한 연도별 시험 포장(2018+2020년, 2019년)을 나누어 생체중 모델 개발한 결과 단년도(2019년)의 성능이 높게 나타났다. 작부 체계별 적산온도가 유사한 검증과 기존 검증 간 비교 결과 단작 포장은 RMSE 및 RE를 기준으로 각각 29.1%와 34.3%로 개선되었으나 이모작 포장은 -19.6%, -31.3%로 저하되었다. 적산온도 이외의 환경 요인, 분광 및 생육 데이터 추가 시 다양한 환경 조건에서 재배되는 콩 생육을 추정 가능할 것으로 판단된다.
신차와 중고차가 함께 경쟁하는 시장에서 신차의 경쟁만을 모형화한다면 가격이나 기타 프로모션 탄력성의 추정이 왜곡될 수 있다. 그러나 자동차 시장을 연구대상으로 한 선행연구의 대부분이 신차 시장의 경쟁에만 관심을 기울였던 바, 합리적인 가격결정이나 프로모션 기획에 도움을 주기에 미흡한 점이 있었다. 본 연구는 신차의 가격결정 및 프로모션 기획이 향후 중고차 시장을 통해 리바운드되어 신차 매출에 다시 영향을 미친다는 점을 반영하여 모형을 설정하였다. 즉, 서로 다른 신차간의 (혹은 서로 다른 중고차간의) 교차탄력성보다, 동일 모델의 신차와 중고차간의 교차탄력성이 높다는 가정하에 모형을 설정하였다. 방법론적으로는 네스티드 로짓(Nested Logit) 모형을 설정하여 소비자의 자동차 선택은 단계적으로 이루어진다고 가정하였다. 즉, 1단계에서 자동차 모델을 선택하고, 모델이 정해지면 2단계에서 신차와 중고차 중 선택하는 구조를 가정하였다 실증분석은 미국 전역에서 2009년 1월부터 2009년 6월까지 판매된 모든 컴팩트 카 모델 중에서 시장점유율 상위 9개 모델의 신차와 중고차를 대상으로 하였다. 실증분석을 통하여 비교 대상 모형보다 제안된 모형이 모형 적합도 측면에서 우월하고 예측타당성도 높다는 것을 보여주었다. 제안된 모형으로 부터 추정된 모수를 사용하여 몇 가지 시나리오를 상정하여 시뮬레이션을 실시한 결과, 신차(중고차)가 점유율을 높이고자 리베이트를 실시할 경우 중고차(신차)는 현재의 시장점유율을 유지하기 위해 대응 가격할인을 실시하게 되는데 할인 폭은 반대의 경우에 비해 높다는(낮다는)점을 확인하였다. 또한 시뮬레이션 결과가 시사하는 바는 신차와 중고차가 함께 경쟁하는 시장에서 IIA(Independence of Irrelevant Alternatives)모형을 적용할 경우 동일모델의 신차와 중고차간의 교차 탄력성을 과소평가하게 되어 현상유지를 위한 가격할인을 실시할 경우 적정한 수준이하로 하게 된다는 것이다.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.