Estimation of Leaf Area Index in a Mountain Forest of Central Japan with a 30-m Spatial Resolution Based on Landsat Operational Land Imager Imagery: An Application of a Simple Model for Seasonal Monitoring

Melnikova, Irina; Awaya, Y.; Saitoh, Taku M.; Muraoka, Hiroyuki; Sasai, T.

doi:10.3390/rs10020179

Cited by 15 publications

(15 citation statements)

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“…Indeed, the IAV of the d4PDF climate variables is lower than the average of the gridded climate data sets (Table S1), although they are usually not the lowest of all the data sets. The lower resolution of the BEAMS estimates compared with that of the in situ data may also contribute to the smaller GPP IAV because it flattens over large area with heterotrophic topography and vegetation (Melnikova et al, ). We assume all three of these factors contribute to the underestimation of GPP IAV by BEAMS compared with in situ observations.…”

Section: Resultsmentioning

confidence: 99%

“…Because LAI and FAPAR are related by the Beer–Lambert law according to the Monsi‐Saeki theory (Melnikova et al, ) and because not all of the satellite data sets provide FAPAR, we evaluated only the derived LAI by comparing it with several satellite data sets, as described in Table . In addition, we compared the estimated LAI with the LAI of prognostic models of the global 0.5° Model outputs of Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP; Huntzinger et al, ) and the LAI of the Global Phenology Reanalysis (PhenoAnalysis) of Stöckli et al ().…”

Section: Methodsmentioning

confidence: 99%

“…We derived the vegetation variables LAI and FAPAR by integrating satellite observations into prognostic air temperature-based estimations. Previous studies revealed the dependence of LAI on temperature (Kunkel et al, 2004;Melnikova et al, 2018). In this study, we derived LAI and FAPAR using the 30-year satellite LAI and FAPAR data set GIMMS3g from Boston University and the d4PDF air temperature.…”

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confidence: 99%

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Effects of Anthropogenic Activity on Global Terrestrial Gross Primary Production

Melnikova

Sasai

2020

JGR Biogeosciences

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Gross primary production (GPP) has been identified as the largest terrestrial carbon flux and the major driver of the growing biosphere uptake of carbon. Factorial simulations using several biosphere models have been used to estimate the effects of long‐term (>50 years) climate change on global terrestrial GPP. However, no study has integrated large‐ensemble climate simulation data into a biosphere model to realistically estimate global terrestrial GPP with associated uncertainty. Here we present a novel approach to estimate the global terrestrial long‐term GPP with associated climate data‐induced uncertainty that combines a diagnostic‐type biosphere model with a large‐ensemble climate simulation data set. We distinguish the effects of recent anthropogenic activity on global GPP (the anthropogenic GPP effect) from the effects of interannual climate variability (the natural GPP effect) by comparing GPP model estimates forced by historical and “nonwarming” climate data. We provide evidence for an increasing anthropogenic effect on global terrestrial GPP. The anthropogenic GPP effect is driven by CO2 fertilization, which is projected to weaken or saturate by 2050–2150, depending on the representative concentration pathway scenario used. Model results suggest that shortwave radiation couples with El Niño–Southern Oscillation conditions and volcanic eruptions to drive the natural GPP effect. Because shortwave radiation at the surface is related to cloud cover, we encourage future studies to focus on cloud‐radiation feedbacks on the carbon cycle.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Effects of Anthropogenic Activity on Global Terrestrial Gross Primary Production

Melnikova

Sasai

2020

JGR Biogeosciences

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“…Adhikari et al examined the effect of C-Correction on fractional tree cover and found that ratio-based vegetation indices were not affected significantly [11]. Leaf Area Index model using Minnaert topographic correction has succeeded in improving the result, but this previous research did not compare the result to the uncorrected image [13]. Information regarding canopy density is important since the quality of vegetation stands can be figured out from this information; regardless, there is no change to the extent [14], [15].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area

Umarhadi¹,

Danoedoro²

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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One of the main factors contributing to radiometric distortion on remote sensing data is a topographic effect, but it can be reduced by applying the topographic correction. This study identifies the effect of topographic correction on canopy density mapping in the Menoreh Mountains, Indonesia. Topographic correction methods examined in this research are C-Correction, Minnaert, and Sun-Canopy-Sensor+C (SCS+C). Canopy density estimation was approached using vegetation indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Aerosol Free Vegetation Index (AFRI) 1.6, and AFRI 2.1 derived from Landsat-8 OLI imagery. We evaluated the performance of topographic correction by visual and statistical analysis before comparing the accuracy of canopy density estimation of different vegetation indices and correction methods. The results showed that topographic normalization could increase the accuracy of canopy density mapping. The most significant improvement is the model using MSAVI, which increases by 1.207% using the Minnaert method to reach 86.692% accuracy. Meanwhile, NDVI, AFRI 1.6, and AFRI 2.1 have less significant improvement with the maximum increase of 0.241%, 0.057%, and 0.032% using the SCS+C method, reaching the accuracy of 88.980%, 83.303%, and 82.308%, respectively. The accuracies were slightly improved since the algorithms have already reduced the effect of topography, which are categorized as ratio vegetation indices. SCS+C is the best topographic correction method because of not only the appropriate assumption of canopy normalization but also its consistency and better accuracy in canopy density estimation, among other methods.

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“…However, the quality of remote sensing images is vulnerable to solar altitude, atmosphere conditions, and terrain-induced shadows, which lead to inaccuracies in spectral reflectance [14,15]. Due to terrain variation, the geometric relationships among the earth's surface, sensors, and solar illumination are not stable, thus the solar radiation received at different locations could be significantly different [16]. The areas with the same forest type and similar slopes but different aspects may have different spectral reflectance.…”

Section: Introductionmentioning

confidence: 99%

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

et al. 2019

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Spectral reflectance distortions caused by terrain and solar illumination seriously reduce the accuracy of mapping forest carbon density, especially in mountainous regions. Many models have been developed for mitigating or eliminating the terrain effects on the quality of remote sensing images in hilly and mountainous areas. However, these models usually use global parameters, which may lead to overcorrections for regions with poor illumination and steep slopes. In this study, we present a local parameter estimation (LPE) method based on a pixel-moving window for topographic correction (TC), which can be considered as a general optimization framework for most semiempirical TC models. We set seven kernel sizes for the presented framework, which are 15 pixels, 25 pixels, 50 pixels, 100 pixels, 250 pixels, 500 pixels, and 1000 pixels, respectively. The proposed method was then applied to four traditional TC models, Minnaert (MIN), C Correction (CC), Sun Canopy Sensor + C (SCSC) and Statistical Empirical Correction (SEC), to form four new TC models. These new models were used to estimate forest carbon density of a mountainous area in Southern China using field plot data and a Landsat 8 image. Four evaluation methods, including correlation analysis, the stability of land covers, comparison of reflectance between sunlit and shaded slopes, and accuracy assessment of forest carbon density, were employed to evaluate the contributions of moving window sizes, and assess the performance of the TC models for forest carbon density estimation. The results show that the four TC models with LPE perform much better than the traditional TC models in reducing the topographic effects and improving the estimation accuracy of forest carbon density for the study area. Among the traditional TC models, SEC performs slightly better than SCSC, CC, and MIN. Therefore, the SEC-based model with LPE, that is, LPE-SEC, gets greater R2 and smaller relative RMSE values in estimating forest carbon density than other models. Moreover, all the means of the predicted forest carbon density values fall in the confidence interval of the validation data at a significant level of 0.05. Overall, this study implies that the proposed method with LPE provides great potential to improve the performance of TC and forest carbon density estimation for the study area. It is expected that the improved TC method can be applied to other mountainous areas to improve the quality of remotely sensed images.

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Estimation of Leaf Area Index in a Mountain Forest of Central Japan with a 30-m Spatial Resolution Based on Landsat Operational Land Imager Imagery: An Application of a Simple Model for Seasonal Monitoring

Cited by 15 publications

References 58 publications

Effects of Anthropogenic Activity on Global Terrestrial Gross Primary Production

Effects of Anthropogenic Activity on Global Terrestrial Gross Primary Production

The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

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