Takahisa Maeda

Using digital camera images to detect canopy condition of deciduous broad-leaved trees

Background: Recent studies have described a technique that incorporates a digital camera to observe aspects of tree phenology, such as leaf expansion and leaf fall. This technique has shown that seasonal patterns of red, green and blue digital numbers (RGB_DN) extracted from digital images differ between species. Aims: To identify the different characteristics of phenology between species by examining RGB_DN, the relationship between the seasonal patterns of RGB_DN and ecological characteristics for various species were evaluated throughout the year. Methods: The relationship between the normalised RGB_DN values extracted from digital images and in situ leaf area index (LAI) and leaf chlorophyll content (indicated by soil and plant analyser development, SPAD) was examined for three dominant species for multiple years in a cool-temperate, deciduous, broad-leaved forest in Japan. Results: The RGB_DN values in spring were not useful in detecting the different characteristics of leaf-flush patterns between species. In contrast, RGB_DN values in autumn showed differences in leaf-colouring as well as in leaf-fall patterns and timings between species. Conclusion: Differences in autumn phenology between tree species can be detected by using the normalised RGB_DN technique, while the technique cannot be applied in spring.

Seasonal variations of gas exchange and water relations in deciduous and evergreen trees in monsoonal dry forests of Thailand

This study compared leaf gas exchange, leaf hydraulic conductance, twig hydraulic conductivity and leaf osmotic potential at full turgor between two drought-deciduous trees, Vitex peduncularis Wall. and Xylia xylocarpa (Roxb.) W. Theob., and two evergreen trees, Hopea ferrea Lanessan and Syzygium cumini (L.) Skeels, at the uppermost canopies in tropical dry forests in Thailand. The aims were to examine (i) whether leaf and twig hydraulic properties differ in relation to leaf phenology and (ii) whether xylem cavitation is a determinant of leaf shedding during the dry season. The variations in almost all hydraulic traits were more dependent on species than on leaf phenology. Evergreen Hopea exhibited the lowest leaf-area-specific twig hydraulic conductivity (leaf-area-specific K(twig)), lamina hydraulic conductance (K(lamina)) and leaf osmotic potential at full turgor (Ψ(o)) among species, whereas evergreen Syzygium exhibited the highest leaf-area-specific K(twig), K(lamina) and Ψ(o). Deciduous Xylia had the highest sapwood-area-specific K(twig), along with the lowest Huber value (sapwood area/leaf area). More negative osmotic Ψ(o) and leaf osmotic adjustment during the dry season were found in deciduous Vitex and evergreen Hopea, accompanied by low sapwood-area-specific K(twig). Regarding seasonal changes in hydraulics, no remarkable decrease in K(lamina) and K(twig) was found during the dry season in any species. Results suggest that leaf shedding during the dry season is not always associated with extensive xylem cavitation.

Dataset of CarboEastAsia and uncertainties in the CO2 budget evaluation caused by different data processing

The datasets of net ecosystem CO2 exchange (NEE) were acquired from 21 forests, 3 grasslands, and 3 croplands in the eastern part of Asia based on the eddy covariance measurements of the international joint program, CarboEastAsia. The program was conducted by three networks in Asia, ChinaFLUX, JapanFlux, and KoFlux, to quantify, synthesize, and understand the carbon budget of the eastern part of Asia. An intercomparison was conducted for NEE estimated by three gap-filling procedures adopted by ChinaFLUX, JapanFlux, and KoFlux to test the range of uncertainty in the estimation of NEE. The overall comparison indicated good agreement among the procedures in the seasonal patterns of NEE, although a bias was observed in dormant seasons depending on the different criteria of data screening. Based on the gap-filled datasets, the magnitude and seasonality of the carbon budget were compared among various biome types, phenology, and stress conditions throughout Asia. The annual values of gross primary production and ecosystem respiration were almost proportional to the annual air temperature. Forest management, including clear-cutting, plantation, and artificial drainage, was significant and obviously affected the annual carbon uptake within the forests. Agricultural management resulted in notable seasonal patterns in the crop sites. The dataset obtained from a variety of biome types would be an essential source of knowledge for ecosystem science as well as a valuable validation dataset for modeling and remote sensing to upscale the carbon budget estimations in Asia.

Classification of arid lands, including soil degradation and irrigated areas, based on vegetation and aridity indices

It is preferable to prepare internally consistent maps of arid regions on a global scale in order to understand the present conditions of arid regions, especially deserts and soil degradation areas. We attempted to delimit arid regions at a global scale by combining climate data, i.e. aridity index (AI), and vegetation data, i.e. vegetation index. The annual AI was estimated by the ratio of mean annual precipitation to mean annual potential evapotranspiration, using the Thornthwaite method. The long-term mean of yearly maximum normalized difference vegetation index (NDVIymx) was used as an indicator of the vegetation condition. Arid regions of the world were classified into four categories, namely A, severe deserts, where both aridity and vegetation indices are very small; G, semi-arid regions, where the vegetation index is proportionally related to the AI; I, irrigated areas and oases, where the vegetation is relatively abundant despite severe dryness; and S, soil degradation areas, where the vegetation is poor despite relatively humid conditions. The Sahel from Niger to Chad, the Sahel in Darfur, and the Ordos Plateau in China are within Category S. The standard deviation of NDVIymx is very small/large in severe deserts/semi-arid areas, respectively. Thus, the Sahara desert was clearly distinguished from the Sahel; the latter belongs to Category G and drought occurs frequently here. In Category S zones, the standard deviation of NDVIymx is relatively small compared with that within the Category G zone because the return rainfall does not seem to promptly restore productivity. Category S was divided into three subdivisions according to the degree of degradation, expressed by the ratio of the AI to vegetation index. Category G was also divided into four classes, according to degree of vegetation (or aridity). The distribution of Category S is comparable to the soil degradation areas mapped by Global Assessment of Human-Induced Soil Degradation (GLASOD) data. True deserts, where the standard deviation of NDVIymx is very small, were selected from the ‘severe desert’ group. Desert areas were classified as true deserts, severe deserts, grassland deserts (Category G), and soil degradation deserts (Category S).

8 million phenological and sky images from 29 ecosystems from the Arctic to the tropics: the Phenological Eyes Network

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.