Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.
Rapid climate change and intensified human activities have resulted in water table lowering (WTL) and enhanced nitrogen (N) deposition in Tibetan alpine wetlands. These changes may alter the magnitude and direction of greenhouse gas (GHG) emissions, affecting the climate impact of these fragile ecosystems. We conducted a mesocosm experiment combined with a metagenomics approach (GeoChip 5.0) to elucidate the effects of WTL (-20 cm relative to control) and N deposition (30 kg N ha-1 yr-1 ) on carbon dioxide (CO2 ), methane (CH4 ) and nitrous oxide (N2 O) fluxes as well as the underlying mechanisms. Our results showed that WTL reduced CH4 emissions by 57.4% averaged over three growing seasons compared with no-WTL plots, but had no significant effect on net CO2 uptake or N2 O flux. N deposition increased net CO2 uptake by 25.2% in comparison with no-N deposition plots and turned the mesocosms from N2 O sinks to N2 O sources, but had little influence on CH4 emissions. The interactions between WTL and N deposition were not detected in all GHG emissions. As a result, WTL and N deposition both reduced the global warming potential (GWP) of growing season GHG budgets on a 100-year time horizon, but via different mechanisms. WTL reduced GWP from 337.3 to -480.1 g CO2 -eq m-2 mostly because of decreased CH4 emissions, while N deposition reduced GWP from 21.0 to -163.8 g CO2 -eq m-2 , mainly owing to increased net CO2 uptake. GeoChip analysis revealed that decreased CH4 production potential, rather than increased CH4 oxidation potential, may lead to the reduction in net CH4 emissions, and decreased nitrification potential and increased denitrification potential affected N2 O fluxes under WTL conditions. Our study highlights the importance of microbial mechanisms in regulating ecosystem-scale GHG responses to environmental changes.
Debris flows threaten human life security and property in its destroyed and buried way, especially dangerous to cities, which destroyed most seriously to the main structure-houses in the cities. This paper takes Dongchuan, a district of Kunming, Yunnan province as an example to explore the solution of estimating housing loss caused by urban debris flow disaster. Firstly, using spot-5 data to extract housing information. Then dividing Dongchuan urban areas into three different zones at dangerous level using two-dimensional mathematical model of debris flow and numerical simulation methods: high danger zone, medium danger zone and low danger zone. When dividing different danger zones. Lastly, using the spatial analysis function of ArcGIS Desktop 9.2 to finish building loss calculation and estimation in different danger zone.
Abstract High soil organic carbon content, extensive root biomass, and low nutrient availability make alpine grasslands an important ecosystem for assessing the influence of nutrient enrichment on soil respiration (SR). We conducted a four-year (2009–2012) field experiment in an alpine grassland on the Qinghai-Tibetan Plateau to examine the individual and combined effects of nitrogen (N, 100 kg ha −1 year −1 ) and phosphorus (P, 50 kg ha −1 year −1 ) addition on SR. We found that both N and P addition did not affect the overall growing-season SR but effects varied by year: with N addition SR increased in the first year but decreased during the last two years. However, while P addition did not affect SR during the first two years, SR increased during the last two years. No interactive effects of N and P addition were observed, and both N addition and P addition reduced heterotrophic respiration during the last year of the experiment. N and P addition affected SR via different processes: N mainly affected heterotrophic respiration, whereas P largely influenced autotrophic respiration. Our results highlight the divergent effects of N and P addition on SR and address the important potential of P enrichment for regulating SR and the carbon balance in alpine grasslands.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)