Salt-tolerant plant moderates the effect of salinity on soil organic carbon mineralization in a subtropical tidal wetland.

Although salinization is widely known to affect cycling of soil carbon (C) in tidal freshwater wetlands, the role of the presence or absence of plants in mediating the responses of soil organic carbon (SOC) mineralization to salinization is poorly understood. In this study, we translocated soils collected from a tidal freshwater wetland to sites with varying salinities along a subtropical estuarine gradient and established unplanted and planted (with the salt-tolerant plant Cyperus malaccensis Lam.) mesocosms at each site. We simultaneously investigated cumulative soil CO2 emissions, C-acquiring enzyme activities, availability of labile organic C (LOC), and structures of bacterial and fungal communities. Overall, in the planted mesocosm, the soil LOC content and the activities of ß-1,4-glucosidase, cellobiohydrolase, phenol oxidase, and peroxidase increased with salinization. However, in the unplanted mesocosm, soil LOC content decreased with increasing salinity, whereas all the C-acquiring enzyme activities did not change. In addition, salinization favored the dominance of bacterial and fungal copiotrophs (e.g., γ-Proteobacteria, Bacteroidetes, Firmicutes, and Ascomycota) in the planted mesocosms. Contrarily, in the unplanted mesocosms salinization favored bacterial and fungal oligotrophs (e.g., α-Proteobacteria, Chloroflexi, Acidobacteria, and Basidiomycota). In both planted and unplanted mesocosms, cumulative soil CO2 emissions were affected by soil LOC content, activities of C-acquiring enzymes, and microbial C-use trophic strategies. Overall, cumulative soil CO2 emissions increased by 35% with increasing salinity in the planted mesocosm but decreased by 37% as salinity increased in the unplanted mesocosm. Our results demonstrate that the presence or absence of salt-tolerant plants can moderate the effect of salinity on SOC mineralization in tidal wetland soils. Future C prediction models should embed both planted and unplanted modules to accurately simulate cycling of soil C in tidal wetlands under sea level rise.

Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images.

Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong'an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale.

[Construction of age group vegetation index and preliminary application].

In the present paper, one remote sensing index-age group vegetation index (AGVI) was put forward, and its feasibility was verified. Taking 518 groups of pine forest age group data collected in 13 counties (cities) of Sanming, Jiangle, Shaxian, Nanping, Huaan, Yunxiao, Nanping, Anxi, Putian, Changting, Jianyang, Ningde and Fuqing, Fujian Province and HJ-1 CCD multi-spectral image at the same time-phase as the basis, the spectrum differences of blue, green, red, near infrared and NDVI of each age group were analyzed, showing the characteristics of young forest>middle-aged forest>over-mature forest>mature forest>near mature forest at near infrared band and mature forest>near mature forest>over-mature forest>young forest>middle-aged forest at NDVI, thus the age group vegetation index (AGVI) was constructed; the index could increase the absolute and relative spectrum differences among age groups. For the pine forest AGVI, cluster analysis was conducted with K-mean method, showing that the division accuracy of pine forest age group was 80.45%, and the accurate rate was 90.41%. Therefore, the effectiveness of age group vegetation index constructed was confirmed.

[Spectral features analysis of Pinus massoniana with pest of Dendrolimus punctatus Walker and levels detection].

Taking 51 field measured hyperspectral data with different pest levels in Yanping, Fujian Province as objects, the spectral reflectance and first derivative features of 4 levels of healthy, mild, moderate and severe insect pest were analyzed. On the basis of 7 detecting parameters construction, the pest level detecting models were built. The results showed that (1) the spectral reflectance of Pinus massoniana with pests were significantly lower than that of healthy state, and the higher the pest level, the lower the reflectance; (2) with the increase in pest level, the spectral reflectance curves' "green peak" and "red valley" of Pinus massoniana gradually disappeared, and the red edge was leveleds (3) the pest led to spectral "green peak" red shift, red edge position blue shift, but the changes in "red valley" and near-infrared position were complicated; (4) CARI, RES, REA and REDVI were highly relevant to pest levels, and the correlations between REP, RERVI, RENDVI and pest level were weak; (5) the multiple linear regression model with the variables of the 7 detection parameters could effectively detect the pest levels of Dendrolimus punctatus Walker, with both the estimation rate and accuracy above 0.85.

[Construction of vegetation shadow index (SVI) and application effects in four remote sensing images].

Taking the images of Landsat TM, ALOS AVNIR-2, CBERS-02B CCD and HJ-1 CCD as the experimental data, for increasing the differences among shaded area, bright area and water further, the present paper construed a novel vegetation index-Shaded Vegetation Index(SVI), which can not only keep the absolute differences among bright area, shaded area and water area in the near-infrared band, but also can enlarge NDVI, eliminate the possible mixes, and change the histogram "skewed" phenomenon of NDVI, so the vegetation index value is closer to normal distribution, and more in line with the filed condition; this new index was applied to the surface features of large difference of the near-infrared radiation characteristics. Verified by accuracy assessment for the bright area, shaded area and water area recognition effects with SVI, it was showed that the overall classification accuracies of these images were up to 98. 89%, 100%, 97.78% and 97.78% respectively, with the overall Kappa statistics of 0.9833, 1, 0.9667, and 0.966 7, indicating that SVI has excellent detection effects for bright area, shaded area and water area; the statistical comparison of sub-images between SVI and NDVI also illustrated the reliability and effectiveness of SVI, which can be applied in the shadow removal for remote sensing images.