Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage.

BACKGROUND: Fractional vegetation coverage (FVC) is a crucial parameter in determining vegetation structure. Automatic measurement of FVC using digital images captured by mobile smart devices is a potential direction for future research on field survey methods in plant ecology, and this algorithm is crucial for accurate FVC measurement. However, there is a lack of insight into the influence of illumination on the accuracy of FVC measurements. Therefore, the main objective of this research is to assess the adaptiveness and performance of different algorithms under varying light conditions for FVC measurements and to deepen our understanding of the influence of illumination on FVC measurement. METHODS AND RESULTS: Based on a literature survey, we selected four algorithms that have been reported to have high accuracy in automatic FVC measurements. The first algorithm (Fun01) identifies green plants based on the combination of [Formula: see text], [Formula: see text], and [Formula: see text] ([Formula: see text], [Formula: see text], and [Formula: see text] are the actual pixel digital numbers from the images based on each RGB channel, [Formula: see text] is the abbreviation of the Excess Green index), the second algorithm (Fun02) is a decision tree that uses color properties to discriminate plants from the background, the third algorithm (Fun03) uses [Formula: see text] ([Formula: see text] is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses [Formula: see text] and [Formula: see text] to separate the plants from the background. [Formula: see text] is an algorithm used to determine a threshold to transform the image into a binary image for the vegetation and background. We measured the FVC of several surveyed quadrats using these four algorithms under three scenarios, namely overcast sky, solar forenoon, and solar noon. FVC values obtained using the Photoshop-assisted manual identification method were used as a reference to assess the accuracy of the four algorithms selected. Results indicate that under the overcast sky scenario, Fun01 was more accurate than the other algorithms and the MAPE (mean absolute percentage error), BIAS, relBIAS (relative BIAS), RMSE (root mean square error), and relRMSE (relative RMSE) are 8.68%, 1.3, 3.97, 3.13, and 12.33%, respectively. Under the scenario of the solar forenoon, Fun02 (decision tree) was more accurate than other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 22.70%, - 2.86, - 7.70, 5.00, and 41.23%. Under the solar noon scenario, Fun02 was also more accurate than the other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 20.60%, - 6.39, - 20.67, 7.30, and 24.49%, respectively. CONCLUSIONS: Given that each algorithm has its own optimal application scenario, among the four algorithms selected, Fun01 (the combination of [Formula: see text], [Formula: see text], and [Formula: see text]) can be recommended for measuring FVC on cloudy days. Fun02 (decision tree) is more suitable for measuring the FVC on sunny days. However, it considerably underestimates the FVC in most cases. We expect the findings of this study to serve as a useful reference for automatic vegetation cover measurements.

A handheld device for measuring the diameter at breast height of individual trees using laser ranging and deep-learning based image recognition.

BACKGROUND: Accurate and efficient measurement of the diameter at breast height (DBH) of individual trees is essential for forest inventories, ecological management, and carbon budget estimation. However, traditional diameter tapes are still the most widely used dendrometers in forest surveys, which makes DBH measurement time-consuming and labor-intensive. Automatic and easy-to-use devices for measuring DBH are highly anticipated in forest surveys. In this study, we present a handheld device for measuring the DBH of individual trees that uses digital cameras and laser ranging, allowing for an instant, automated, and contactless measurement of DBH. RESULTS: The base hardware of this device is a digital camera and a laser rangefinder, which are used to take a picture of the targeted tree trunk and record the horizontal distance between the digital camera and the targeted tree, respectively. The core software is composed of lightweight convolutional neural networks (CNNs), which includes an attention-focused mechanism for detecting the tree trunk to log the number of pixels between the edges. We also calibrated the digital camera to correct the distortion introduced by the lens system, and obtained the normalized focal length. Parameters including the horizontal distance between the digital camera and the targeted tree, number of pixels between the edges of the tree trunk, and normalized focal length were used to calculate the DBH based on the principles of geometrical optics. The measured diameter values, and the longitudes and latitudes of the measurement sites, were recorded in a text file, which is convenient to export to external flash disks. The field measurement accuracy test showed that the BIAS of the newly developed device was - 1.78 mm, and no significant differences were found between the measured diameter values and the true values (measured by the conventional tape). Furthermore, compared with most other image-based instruments, our device showed higher measurement accuracy. CONCLUSIONS: The newly developed handheld device realized efficient, accurate, instant, and non-contact measurements of DBH, and the CNNs were proven to be successful in the detection of the tree trunk in our research. We believe that the newly developed device can fulfill the precision requirement in forest surveys, and that the application of this device can improve the efficiency of DBH measurements in forest surveys.

Internet of Things to network smart devices for ecosystem monitoring.

Smart, real-time, low-cost, and distributed ecosystem monitoring is essential for understanding and managing rapidly changing ecosystems. However, new techniques in the big data era have rarely been introduced into operational ecosystem monitoring, particularly for fragile ecosystems in remote areas. We introduce the Internet of Things (IoT) techniques to establish a prototype ecosystem monitoring system by developing innovative smart devices and using IoT technologies for ecosystem monitoring in isolated environments. The developed smart devices include four categories: large-scale and nonintrusive instruments to measure evapotranspiration and soil moisture, in situ observing systems for CO2 and δ13C associated with soil respiration, portable and distributed devices for monitoring vegetation variables, and Bi-CMOS cameras and pressure trigger sensors for terrestrial vertebrate monitoring. These new devices outperform conventional devices and are connected to each other via wireless communication networks. The breakthroughs in the ecosystem monitoring IoT include new data loggers and long-distance wireless sensor network technology that supports the rapid transmission of data from devices to wireless networks. The applicability of this ecosystem monitoring IoT is verified in three fragile ecosystems, including a karst rocky desertification area, the National Park for Amur Tigers, and the oasis-desert ecotone in China. By integrating these devices and technologies with an ecosystem monitoring information system, a seamless data acquisition, transmission, processing, and application IoT is created. The establishment of this ecosystem monitoring IoT will serve as a new paradigm for ecosystem monitoring and therefore provide a platform for ecosystem management and decision making in the era of big data.

Effects of elevated CO2 and N addition on growth and N2 fixation of a legume subshrub (Caragana microphylla Lam.) in temperate grassland in China.

It is well demonstrated that the responses of plants to elevated atmospheric CO(2) concentration are species-specific and dependent on environmental conditions. We investigated the responses of a subshrub legume species, Caragana microphylla Lam., to elevated CO(2) and nitrogen (N) addition using open-top chambers in a semiarid temperate grassland in northern China for three years. Measured variables include leaf photosynthetic rate, shoot biomass, root biomass, symbiotic nitrogenase activity, and leaf N content. Symbiotic nitrogenase activity was determined by the C(2)H(2) reduction method. Elevated CO(2) enhanced photosynthesis and shoot biomass by 83% and 25%, respectively, and the enhancement of shoot biomass was significant only at a high N concentration. In addition, the photosynthetic capacity of C. microphylla did not show down-regulation under elevated CO(2). Elevated CO(2) had no significant effect on root biomass, symbiotic nitrogenase activity and leaf N content. Under elevated CO(2), N addition stimulated photosynthesis and shoot biomass. By contrast, N addition strongly inhibited symbiotic nitrogenase activity and slightly increased leaf N content of C. microphylla under both CO(2) levels, and had no significant effect on root biomass. The effect of elevated CO(2) and N addition on C. microphylla did not show interannual variation, except for the effect of N addition on leaf N content. These results indicate that shoot growth of C. microphylla is more sensitive to elevated CO(2) than is root growth. The stimulation of shoot growth of C. microphylla under elevated CO(2) or N addition is not associated with changes in N(2)-fixation. Additionally, elevated CO(2) and N addition interacted to affect shoot growth of C. microphylla with a stimulatory effect occurring only under combination of these two factors.

[A new assessment method for the quality of ecological monitoring data: taking CERN's tree growth dataset as a case].

This paper presented a new and simple assessment method for the quality of ecological monitoring data. This method theorized the associations between the data reliability as an ordinal variable with different number of classes and the data sources such as natural main ecological processes, secondary ecological processes, and extraneous and exotic processes, and offered a new data quality index to estimate the quality of the whole dataset by using the reasonableness ratio of observations. The assessment results provided the reliability class of each dataset, good explanations for outlier (or error data) flagging decisions, and quality value of the whole dataset. The method was applied to assess two tree growth datasets from Chinese Ecosystem Research Network (CERN), and the results demonstrated that the new data quality index could quantitatively evaluate the quality of the tree growth datasets. The new method would facilitate the development of corresponding software.

Responses of a dominant temperate grassland plant (Leymus chinensis) to elevated carbon dioxide and nitrogen addition in China.

The impact of elevated atmospheric CO(2) concentration on plant communities is varied and strongly dependent on the dominant species response, as well as nutrient conditions. Responses of a dominant species (Leymus chinensis) to elevated CO(2) and N application were examined with open-top chambers in a typical temperate grassland in northern China for 3 yr. The significant effect of elevated CO(2) on L. chinensis growth was mainly reflected in the higher photosynthetic rates, increased leaf number, larger shoot and root biomass, and higher root/shoot (R/S) ratio. Enhancement of root biomass induced by elevated CO(2) was larger (40%) than that of shoot biomass (9%). In contrast, N application had a significant impact on most growth indices examined in this study, which was reflected in the enhanced aboveground growth and depressed belowground growth. Nitrogen application significantly reduced the R/S ratio by an average of 40%. Nitrogen addition significantly enhanced the proportion of senescent biomass and decreased the proportion of green leaf biomass under elevated CO(2). There were no CO(2) x N interactions on most of the measured variables, except on photosynthetic rate and the proportion of aboveground biomass. Plant growth variables showed high interannual variation. These results indicate that belowground growth of L. chinensis is more sensitive to elevated CO(2) than is the aboveground. Aboveground growth of L. chinensis is much more sensitive to N application than to CO(2) enrichment. Therefore, the effect of elevated CO(2) on L. chinensis steppe is more likely to be underestimated if only aboveground parts are considered.

[Mechanism and universality of Yoda's -3/2 law of self-thinning in plant population].

According to plant modular theory, the Yoda's-3/2 law of self-thinning was theoretically rederived in this paper. With the parameters in the expression of Yoda's-3/2 law being given biological meanings, this article offered universal and rational explanations for relevant data in literatures that could not be explained before. Based on the authors' hypothesis, the experiments on the self-thinning process of spring wheat (Triticum aestivum L.) were conducted. Individual morphological and population quantitative indexes were examined at regular intervals. The results showed that the self-thinning process of spring wheat conformed to Yoda's-3/2 law of self-thinning. At the same time, the universality of Yoda's-3/2 law of self-thinning was explained theoretically by analyzing individual morphological data.