Autumn leaf coloring is an essential cultural ecosystem service, but mechanisms of color brightness have scarcely been focused on, even though the autumn color crisis (leaf fall without coloring) has been reported in Japanese alpine shrubs. To approach the mechanisms, we analyzed the relationship between brightness and environmental conditions and projected possible future changes. Autumn color brightness was measured by repeated camera observations at three Japanese alpine sites. Environmental factors were analyzed using linear mixed model comparisons. Results showed earlier green-up timing was related to a duller autumn leaf color, possibly because a prolonged leaf period creates older leaves and reduces physiological vitality in autumn, affecting anthocyanin biosynthesis. Green-up timing based on snowmelt day and degree-day-based prediction of snowmelt day predicted earlier snowmelt and green-up in the future, with decreases in autumn color brightness. The amount of color reduction varied among climate models and scenarios, from a decline of several percent under MRI-CGCM3 RCP 2.6 to around 15% under MIROC5 RCP8.5 by the end of this century. Projected reductions in autumn color brightness could have an economic impact on tourism, and it could also be linked to modifications in material cycles and the ecosystem’s productivity.

Eddy covariance observations play a pivotal role in understanding the land–atmosphere exchange of energy, water, carbon dioxide (CO2), and other trace gases, as well as the global carbon cycle and earth system. To promote the networking of individual measurements and the sharing of data, FLUXNET links regional networks of researchers studying land–atmosphere processes. JapanFlux was established in 2006 as a national branch of AsiaFlux. Despite the growing amount of shared data globally, the availability in Asia is currently limited. In this study, we developed an open dataset of the eddy covariance observations for Japan and East Asia, called JapanFlux2024, that was conducted by researchers affiliated with Japanese research institutions. The data were processed using selected standard methods from the FLUXNET community, with adaptations specific to the JapanFlux2024 dataset. Here, we present the data description and data processing and show the value of processed fluxes of sensible heat, latent heat, and CO2. The dataset will facilitate important studies for Japan and East Asia, such as land–atmosphere interactions, improvement of process models, and upscaling fluxes using machine learning and remote sensing technology, as well as bridge collaborations between Asia and FLUXNET.

Abstract Alpine ecosystems are particularly vulnerable to climate change. Monitoring the distribution of alpine vegetation is required to plan practical conservation activities. However, conventional field observations, airborne and satellite remote sensing are difficult in terms of coverage, cost and resolution in alpine areas. Ground‐based time‐lapse cameras have been used to observe the regions' snowmelt and vegetation phenology and offer significant advantages in terms of cost, resolution and frequency. However, they have not been used in research monitoring of vegetation distribution patterns. This study proposes a novel method for drawing georeferenced vegetation classification maps from ground‐based imagery of alpine regions. Our approach had two components: vegetation classification and georectification. The proposed vegetation classification method uses a pixel time series acquired from fall images, utilizing the fall leaf color patterns. We demonstrated that the performance of the vegetation classification could be improved using time‐lapse imagery and a Recurrent Neural Network. We also developed a novel method to accurately transform ground‐based images into georeferenced data. We propose the following approaches: (1) an automated procedure to acquire Ground Control Points and (2) a camera model that considers lens distortions for accurate georectification. We demonstrated that the proposed approach outperforms conventional methods, in addition to achieving sufficient accuracy to observe the vegetation distribution on a plant‐community scale. The evaluation revealed an F1 score and root‐mean‐square error of 0.937 and 3.4 m in the vegetation classification and georectification, respectively. Our results highlight the potential of inexpensive time‐lapse cameras to monitor the distribution of alpine vegetation. The proposed method can significantly contribute to the effective conservation planning of alpine ecosystems.

At a tower site in a Japanese deciduous needle-leaf forest, which is the observation base of the AsiaFlux and JapanFlux network, we have identified various issues related to the reliability of observations and have accumulated technical knowledge. In this paper, we will introduce our technical efforts to improve the consistency and distribution of observation data for spectral radiation and photosynthetically active radiation. In particular, we report on measures to prevent the aging of the light quantum sensor for measuring photosynthetically effective radiation, and on the development of a device to exclude the effect of reflections from tower structures on the measurement of light reflected from vegetation.

The photochemical reflectance index (PRI) and the chlorophyll carotenoid index (CCI) are carotenoid-sensitive vegetation indices, which can monitor vegetation’s photosynthetic activities. One unique satellite named “Global Change Observation Mission-Climate (GCOM-C)” is equipped with a sensor, “Second Generation Global Imager (SGLI)”, which has the potential to frequently and simultaneously observe PRI and CCI over a wide swath. However, the observation accuracy of PRI and CCI derived from GCOM-C/SGLI remains unclear in forests. Thus, we demonstrated their accuracy assessment by comparing them with in situ data. We collected in situ spectral irradiance data at four forest sites in Japan for three years. We statistically compared satellite PRI with in situ PRI, and satellite CCI with in situ CCI. From the obtained results, the satellite PRI showed poor agreement (the best: r=0.294 (p<0.05)) and the satellite CCI showed good agreement (the best: r=0.911 (p<0.001)). The greater agreement of satellite CCI is possibly because satellite CCI contained fewer outliers and satellite CCI was more resistant to small noise, compared to satellite PRI. Our results suggest that the satellite CCI is more suitable for practical use than the satellite PRI with the latest version (version 3) of GCOM-C/SGLI’s products.

A constant ratio of photosynthetically active radiation (PAR) to solar radiation (SR) (about 0.45-0.46) has been used in many ecological studies to convert SR into PAR by multiplication. The constant ratio is useful and convenient. However, there is no general agreement on whether the ratio is 0.45 or 0.46 and how distributed globally. Accordingly, many local empirical ratios have been reported. This study aims to demonstrate a global distribution of the ratio and its changing range. Thus, we created two simple empirical models to estimate the ratio based on the in-situ climatic data to achieve the aim. The models were created based on accurate data of SR and PAR observed using a direct and diffuse separation method at Tateno in Tsukuba, Japan. At three validation sites in Japan, the ratio could be estimated with an error within 3%, a considerable reduction from 15% in using a constant. The numerical model also produced the ratio within approximately 3% errors. Using the proposed model, we demonstrated that the annual mean of the ratio had a range from 0.409 to 0.477. The results will contribute to the uncertainty estimation when using a constant ratio.

Upland soils are thought to be a sink of CH4, the second most important anthropogenic greenhouse gas, owing to oxidation by methanotrophs. To better understand CH4 fluxes in upland forests, we quasi-continuously measured CH4 fluxes using an automated closed chamber system over seven years on a larch plantation in a volcanic soil in Japan. We hypothesized that the long-term data sufficiently can calibrate modules for CH4 fluxes, and aimed to predict future pathways of CH4 uptake and their uncertainties in the forest. Based on the observations, a thinning of the overstory only marginally influenced the CH4 fluxes measured by the chambers. Using the data with a Bayesian method, we calibrated four modules for CH4 fluxes in forest soils, which were embedded in the process-based ecosystem model VISIT. The modules well reproduced the observed seasonality, annual budgets, and interannual variability in the CH4 fluxes after calibrating the following parameters: the diffusion coefficient or base CH4 oxidation rate constant and temperature sensitivity. The CH4 fluxes were predicted to increase in the future under the RCP8.5 scenario but to decrease under the RCP 2.6 scenario. The contrasting trajectory was caused by rising and decreasing CH4 concentrations under the RCP 8.5 and 2.6 scenarios, respectively. Furthermore, the magnitudes of the future changes in the fluxes differed in each module because the responses to the changes in the CH4 concentrations were inconsistent among the modules. The observed CH4 fluxes increased with increasing atmospheric CH4 concentration (4.95 mg CH4 m(-2) d(-1) ppm(-1)), which was greater in magnitude than those in the modules. Considering the uncertainties in the modules and potential confounding effects in the observations, we conclude that further understanding the responses of CH4 uptake to rising CH4 concentrations is required.

Liquid crystal tunable filter (LCTF) can change the transmissible wavelength by changing the applied voltage to the filter, which enables the drastic increase in the observable wavelength resolution in a small size system and is considered to be a powerful tool for the spectral earth observation from flying units or microsatellites. However, there is limited knowledge about its season-long application for the vegetation monitoring and the prediction of the ecosystem photosynthetic capacity. We compared the seasonal variation of spectral reflectance obtained by a LCTF camera with that obtained by a popular spectral radiometer in a cool-temperate young larch plantation in northern Hokkaido, Japan. Then we tried to find the best normalized difference spectral index (NDSI) to explain the seasonal variation of the ecosystem photosynthetic capacity using all pairs of two reflectances observed in the range of wavelength between 500 and 770 nm with 10-nm intervals (28 wavelength bands) by the LCTF. The best NDSI among all combinations (28×27) of two reflectances was NDSI[770, 720] for the maximum gross primary production at light saturation and NDSI[530, 600] for the initial slope of the light-response curve, which reflect the red edge shift owing to the change in the chlorophyll content and relative strength of the light absorbance in the visible red wavelength region compared with that in the green wavelength region, respectively. Predicted daily gross primary production of the plantation using these NDSI agreed well with the observed values. NDSI[530, 600] was better to distinguish each vegetation type of the studied plantation.

In situ accurate data sets of leaf area index (LAI), above-ground biomass (AGB), and fraction of absorbed photosynthetically active radiation (fAPAR) are indispensable to validate and improve ecological products obtained from satellites. In situ data for satellite validation must be created not from a single-point data but from areal data (such as multiple-points data) representing a satellite footprint. Using multiple-points data, the error of in situ data can be calculated statistically. The quantification of the error in the in situ data enables us to evaluate the discrepancy between the satellites' products and the in situ data as the error in the in situ data and the estimation error in the products separately. Besides, the accuracy of the in situ data is required to be much higher than the accuracy of the satellite products which was officially set. To obtain such in situ data, we have established observation sites for typical land cover types in East Asia, from temperate to cool ecosystems: deciduous needle-leaved forest (DNF), evergreen needle-leaved forest (ENF), deciduous broad-leaved forest (DBF), and grassland (GL). We conducted the observations in 500 m x 500 m areas, which is the footprint scale of the Global Change Observation Mission-Climate satellite. In this paper, the data of LAI, AGB, and fAPAR observed at DNF, DBF, and GL (i.e., except at ENF) are reported. These data are useful even for the validation of other satellite products, especially with higher spatial resolution. Also, the long-term tree census data from 2005 to 2018 at DNF are reported. The complete data set for this abstract published in the Data Paper section of the journal is available in electronic format in MetaCat in JaLTER at .

The Advanced Himawari Imager (AHI) onboard the recently launched next generation geostationary satellite, Himawari-8, provides an opportunity to improve Land Surface Phenology (LSP) detections over the Asia-Pacific region. In this paper, we detected four phenological transition dates (PTDs) using the three-day Two-band Enhanced Vegetation Index (EVI2) time series from AHI based on the Hybrid Piecewise Logistic Model-Land Surface Phenology Detection (HPLM-LSPD) algorithm. The four PTDs are Start of Spring (SOS), End of Spring (EOS), Start of Fall (SOF) and End of Fall (SOF). We evaluated the four AHI-derived PTDs against those detected using eight-day EVI2 time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the polar-orbiting satellite Terra, and three-day Green Chromatic Coordinate (GCC) time series from the Phenological Eyes Network (PEN) at six sites in central and northern Japan. The evaluation was performed by conducting regression analyses, and calculating root mean square difference (RMSD) and bias between satellite- and PEN-derived PTDs. First, the difference in the spatial variations of SOS and EOF timing between naturally vegetated areas, and urban areas and croplands indicates the anthropogenic footprints on LSP. Second, the RMSD of either AHI PTDs or MODIS PTDs against PEN PTDs were higher in the fall (i.e., SOF and EOF) than those in spring (i.e., SOS and EOS). Third, the later EOS and earlier SOF derived from satellite EVI2 relative to those derived from PEN GCC might be caused by the difference in the sensitivity of GCC and EVI2 to the increases in leaf area index (LAI) over high-LAI canopies. Fourth, the higher temporal resolution of AHI EVI2 only helped reduce the RMSD during spring compared to the RMSD for MODIS. In contrast, the RMSD for AHI PTDs and MODIS PTDs were comparable in fall. Finally, the between-sensor correlation in the spatiotemporal variability of the four PTDs was higher for SOS and EOF than those for EOS and SOF.

To validate terrestrial ecological products of the Global Change Observation Mission-Climate (GCOM-C) satellite, a large-scale ecological observation project "JAXA Super Sites 500" was initiated. The project's purpose is to obtain the representative leaf area index (LAI), above-ground biomass (AGB), and fraction of absorbed PAR (fAPAR) in the satellite footprint scale. This study aimed to determine the appropriate observation methods in the satellite scale for the target land-cover types such as a deciduous broad-leaved forest, evergreen/deciduous needle-leaved forests, and a grassland. As a result of the comparative observations, a combination method using litter-traps and LAI-2200 was adopted as the LAI observation in a forest, a tree census method was adopted as the AGB observation in a forest, and a clipping method was adopted as the AGB and LAI observations in a grassland. LAI, AGB, and fAPAR were observed and were used to validate the GCOM-C terrestrial ecological products.

The vulnerability of alpine ecosystems to climate change, as pointed out by the Intergovernmental Panel on Climate Change (IPCC), and the necessity to monitor alpine zones have been recognized globally. The Japanese alpine zone is characterized by extreme snowfall, and snowmelt time is a key factor in the growth of alpine vegetation. Therefore, in 2011, the National Institute for Environmental Studies (NIES), Japan, initiated long-term monitoring of snowmelt time and ecosystems in the Japanese alpine zone using automated digital time-lapse cameras. Twenty-nine monitoring sites are currently in operation. In this study, images from the cameras installed at mountain lodges in Nagano Prefecture and around Mt. Rishiri in Hokkaido are used. In addition, live camera images are obtained from cameras already operated by local governments in the Tohoku area and near Mt. Fuji. Red, green, and blue (RGB) digital numbers are derived from each pixel within the images. Snow-cover and snow-free pixels are classified automatically using a statistical discriminate analysis. Snowmelt time shows site-specific characteristics and yearly variations. It also reflects the local microtopography and differs among the habitats of various functional types of vegetation. The vegetation phenology is quantified using a vegetation index (green ratio) calculated from the RGB digital numbers. By analyzing temporal variations of the green ratio, local distributions of start and end dates and length of growing period are illustrated on a pixel base. The start of the green leaf period corresponds strongly to the snowmelt gradient, and the end of the green leaf period to vegetation type and elevation. The results suggest that the length of the green leaf period mainly corresponds to the snowmelt gradient in relation to local microtopography.

Thinning is a necessary procedure for the sustainable management of plantation forests. Thinning has a remarkable influence on forest carbon balance; however, there are limited studies showing the integrated assessment of the effects of thinning on several understory carbon flux components using long-term monitoring data. We measured understory flux components using automated chambers continuously over 12 years to understand the effects of forest thinning conducted in May 2014 and March 2015 and to elucidate the effect of short-term climate change on understory carbon balance in a Japanese larch (Larix kaempferi Sarg.) forest in central Japan. Although thinning (39% decrease in tree stems) increased the mean annual soil moisture by 16.5% and growing season soil temperature by 2.4%, large inter-annual variations in precipitation and soil temperature largely masked the effects of thinning on both soil respiration (R-s) and soil heterotrophic respiration (Rh). Annual mean soil temperature was positively related to all annual efflux components. Thinning also increased the understory photosynthetic photon flux density (PPFDu) by 63.1% during the growing season (May to October). In post-thinning years, the average three-year understory photosynthesis (GPP(u)), understory plant respiration (R-p), and total understory respiration (R-u) increased significantly by 59.5%, 99.7%, and 26.9%, respectively. The net understory CO2 exchange (NUE = R-u GPP(u)) changed significantly only in 2015 (13.8%). A marginally significant change in NUE based on the three-year average in post-thinning years was also detected (14.4%). Our results indicated each flux component (especially GPP(u), R-p, and R-u) changed dramatically after thinning. In contrast, the NUE of this forest was relatively more robust than other enhanced understory flux components. However, the marginally significant increase of NUE (3-year average in post-thinning years) implied a slight increase in NUE after thinning.

Autumn leaf phenology and its color brightness provide valuable information for managing forest carbon cycles and cultural ecosystem services. Digital repeat photography has provided standard phenological data, but the methodologies for detecting autumn leaf coloring have various strengths and weaknesses. We assessed the accuracy, sensitivity, and uncertainty of various model and color index combinations for detecting autumn leaf coloring. Then we identified the most robust and sensitive methods, using digital repeat photography data from Japanese alpine vegetation. For determining autumn leaf color duration, quadratic or multinomial discriminant analysis using RGB digital numbers had the highest accuracy (hit ratio > 0.7). For determining the peak day of autumn leaf color and its color brightness, we compared uncertainty of methodologies by randomly resampling 80% of the data 20 times to mimic observation errors (e.g., due to heavy rain). The spline-fitted red/green reflectance ratio (RGR) and visible atmospherically resistant index (VARI) proved robust for detecting the peak day (median SD = 1.25). Uncertainty of color brightness was also low when using VARI fitted by a double logistic model for both red and yellow leaves (median coefficient of variation = 1.03). These two indexes are stable despite atmospheric effects, which may result in robustness to daily variation in conditions (e.g., fog). We compared sensitivity of leaf color brightness: RGR and excess red (ExR) fitted by a double logistic model had the highest sensitivity to red and yellow leaves exceeding the average of other combinations by 26% and 88% in median values, respectively. The small denominator or lack of a denominator of these indexes increases the sensitivity to red or yellow. Our results demonstrate the averaged accuracy, sensitivity, and robustness of each methodology among our research sites with different camera observations. These methods should help in utilizing hidden big data from web cameras or past photos that were not intended for scientific research to properly assess autumn leaf phenology and its color brightness.

We report long-term continuous phenological and sky images taken by time-lapse cameras through the Phenological Eyes Network (http://www.pheno-eye.org. Accessed 29 May 2018) in various ecosystems from the Arctic to the tropics. Phenological images are useful in recording the year-to-year variability in the timing of flowering, leaf-flush, leaf-coloring, and leaf-fall and detecting the characteristics of phenological patterns and timing sensitivity among species and ecosystems. They can also help interpret variations in carbon, water, and heat cycling in terrestrial ecosystems, and be used to obtain ground-truth data for the validation of satellite-observed products. Sky images are useful in continuously recording atmospheric conditions and obtaining ground-truth data for the validation of cloud contamination and atmospheric noise present in satellite remote-sensing data. We have taken sky, forest canopy, forest floor, and shoot images of a range of tree species and landscapes, using time-lapse cameras installed on forest floors, towers, and rooftops. In total, 84 time-lapse cameras at 29 sites have taken 8 million images since 1999. Our images provide (1) long-term, continuous detailed records of plant phenology that are more quantitative than in situ visual phenological observations of index trees; (2) basic information to explain the responsiveness, vulnerability, and resilience of ecosystem canopies and their functions and services to changes in climate; and (3) ground-truthing for the validation of satellite remote-sensing observations.