Direct and Lagged Effects of Spring Phenology on Net Primary Productivity in the Alpine Grasslands on the Tibetan Plateau

Exploring the comprehensive link between climatic factors and vegetation productivity in China

Understanding the influence of climatic factors on vegetation dynamics and cumulative effects is critical for global sustainable development. However, the response of vegetation to climate and the underlying mechanisms in different climatic zones remains unclear. In this study, we analyzed the response of vegetation gross primary productivity (GPP) to climatic factors and the cumulative effects across various vegetation types and climatic zones, utilizing data on precipitation (Pr), temperature (Ta), and the standardized precipitation evapotranspiration index (SPEI). The results showed that: (1) GPP showed significant differences among the seven climatic zones, with the highest value observed in zone VII, reaching 1860.07 gC·m- 2, and the lowest in zone I, at 126.03 gC·m- 2. (2) GPP was significantly and positively correlated with temperature in climatic zones I, IV, V, and VI and with precipitation in climatic zones I, II, and IV. Additionally, a significant positive correlated was found between SPEI and GPP in climatic zones I, II, and IV. (3) Drought exerted a cumulative effect on GPP in 45.10% of the regions within China, with an average cumulative duration of 5 months. These effects persisted for 6-8 months in zones I, II, and VII, and for 2-4 months in zones III, IV and VI. Among different vegetation types, forests experienced longest cumulative effect time of 6 months, followed by grasslands (5 months), croplands (4 months), and shrublands (4 months). The cumulative time scale decreased with increasing annual SPEI. The varying responses and accumulation of GPP to drought among different vegetation types in various climatic zones underscore the complexity of vegetation-climate interactions the response and accumulation of GPP to drought.

Remote sensing approach in evaluating anthropogenic impacts on the spatiotemporal changes in net primary productivity of the Niger river basin, from 2000 to 2020

Deterioration of the environment can be examined by utilizing a statistical evaluation of the effects of anthropogenic activities (beneficial or detrimental) on net primary productivity. The Niger River Basin's net primary productivity is significant both theoretically and practically for the management of the natural environment. It is important for her member countries to understand vegetation dynamics, maintain carbon balance, and ensure food security in the region. The research applied remote sensing to determine the relative impact of human activities on the net primary productivity of the Niger River Basin from 2000 to 2020. The study simulated the actual and potential net primary productivity using the Carnegie Ames Stanford Approach and Thornthwaite's Memorial Model respectively, while the result of the simulations was used to calculate human-influenced net primary productivity. The slope of the three simulations was calculated and merged in several scenarios using ArcGIS 10.8 to determine the impact of human activities on net primary productivity of the study area. The negative impacts of human activities were recorded in 89.88 % of the investigated area, while 10.12 % of the NRB had signs of positive impacts. Amongst the biomes, urban areas and bare land experienced the largest negative impacts (97.2 % and 99.8 %, respectively). The study advised the effectiveness of ecological restoration programs, through sound scientific and technical methods, such as those used in rural development, nomadic herding, environmental protection, and natural resource management policies.

The impact of vegetation phenology changes on the relationship between climate and net primary productivity in Yunnan, China, under global warming

Climate influences net primary productivity (NPP) either directly or indirectly via phenology. Therefore, clarifying the indirect effects of climate on NPP through phenology is of utmost importance. However, the underlying mechanisms by which phenology indirectly affects NPP are unknown and poorly studied. Based on different structural equation models, this study analyzed the influence of phenology on the relationship between climate and NPP, and the results were as follows: (1) Temperature and solar radiation directly affect the end and beginning of the growing season, respectively, while precipitation indirectly affects the beginning of the growing season. (2) Spring phenology mainly affects the relationship between subsequent precipitation and net primary productivity, while autumn phenology mainly affects the relationship between temperature and net primary productivity. (3) Solar radiation is the most important direct influence factor on phenology and NPP, and the relationship between it and NPP is hardly disturbed by vegetation phenology. This research holds significant scientific and applied values in enhancing our understanding of the effects of global warming, forecasting ecosystem responses in the future, and formulating adaptation strategies.

Review of Remote Sensing Applications in Grassland Monitoring

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.