Regulation of Vapor Pressure Deficit by Greenhouse Micro-Fog Systems Improved Growth and Productivity of Tomato via Enhancing Photosynthesis during Summer Season

<div><p>The role of a proposed micro-fog system in regulating greenhouse environments and enhancing tomato (<i>Solanum lycopersicum</i> L.) productivity during summer season was studied. Experiments were carried out in a multi-span glass greenhouse, which was divided into two identical compartments involving different environments: (1) without environment control and (2) with a micro-fog system operating when the air vapor pressure deficit (VPD) of greenhouse was higher than 0.5 KPa. The micro-fog system effectively alleviated heat stress and evaporative demand in the greenhouse during summer season. The physiologically favourable environment maintained by micro-fog treatment significantly enhanced elongation of leaf and stem, which contributed to a substantial elevation of final leaf area and shoot biomass. These improvements in physiological and morphological traits resulted in around 12.3% increase of marketable tomato yield per plant. Relative growth rate (RGR) of micro-fog treatment was also significantly higher than control plants, which was mainly determined by the substantial elevation in net assimilation rate (NAR), and to a lesser extent caused by leaf area ratio (LAR). Measurement of leaf gas exchange parameters also demonstrated that micro-fog treatment significantly enhanced leaf photosynthesis capacity. Taken together, manipulation of VPD in greenhouses by micro-fog systems effectively enhanced tomato growth and productivity via improving photosynthesis during summer season.</p></div

Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau

An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68–0.85) and 0.69 (0.64–0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844–0.852) and 0.790 (0.785–0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies

Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau

An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68&ndash;0.85) and 0.69 (0.64&ndash;0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844&ndash;0.852) and 0.790 (0.785&ndash;0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies

Effect of the micro-fog system on plant growth parameters.

<p>RGR (relative growth rate, A), NAR (net assimilation rate, B), and LAR (leaf area ratio, C) were analyzed in plants sampled at 0, 28 and 56 d after transplanting. Values are means±SE (n = 20). Significant difference between humidification and control were examined using Tukey’s test. * Significant at P<0.05, ** Significant at P<0.01, *** Significant at P<0.001. NS: non-significant difference.</p

Effect of the micro-fog system on leaf gas exchange parameters.

<p>Parameters were determined 40 days after transplanting. Values are means±SE (n = 10), significant difference between humidification treatment and control were compared using Tukey’s test. * Significant at P<0.05, ** Significant at P<0.01, *** Significant at P<0.001.</p

Effect of the micro-fog system on plant growth parameters.

<p>RGR (relative growth rate, A), NAR (net assimilation rate, B), and LAR (leaf area ratio, C) were analyzed in plants sampled at 0, 28 and 56 d after transplanting. Values are means±SE (n = 20). Significant difference between humidification and control were examined using Tukey’s test. * Significant at P<0.05, ** Significant at P<0.01, *** Significant at P<0.001. NS: non-significant difference.</p

Effect of the micro-fog system on stomatal traits.

<p>Data represent means±stand error (SE), n = 20. Leaves selected were those for the measurement of leaf gas exchange. Materials were prepared and measured at the same time as gas exchange measurements were taken. Significant difference between humidification treatment and control were compared using Tukey’s test.</p><p>* Significant at P<0.05,</p><p>** Significant at P<0.01,</p><p>*** Significant at P<0.001.</p><p>NS: not significant.</p><p>Effect of the micro-fog system on stomatal traits.</p

Comparison of typical diurnal variation of greenhouse environment.

<p>Air temperature, relatively humidity, VPD between control and humidification treatment were measured at DAT 34 (19 Aug).</p

Effect of the micro-fog system on plant leaf area and shoot biomass production.

<p>Parameters were determined every four weeks. Values are mean±SE (n = 20). Significant difference between humidification treatment and control were compared using Tukey’s test. * Significant at P<0.05, ** Significant at P<0.01, *** Significant at P<0.001.</p