Discriminating the biophysical signal from human-induced effects on long-term primary production dynamics. The case of Patagonia

Resilience response of China's terrestrial ecosystem gross primary productivity under environmental stress

Climate change perturbations contribute to the alteration of ecosystem functions and the reduction of their resilience. Understanding this reduction in resilience is fundamental for formulating strategies for sustainable ecosystem management. Consequently, a systematic evaluation of factors influencing ecosystem resilience is imperative. This involves elucidating the resilience trends and stress conditions within Chinese ecosystems spatially, thereby enabling a holistic assessment of their health status. This study assesses resilience across China by analyzing the one-month lagged time autocorrelation of terrestrial Gross Primary Production (GPP) across China. It differentiates between various stress states within the study region and employs interpretable machine learning methods to examine the relationship between terrestrial ecosystem resilience and environmental changes under different stress conditions. The findings reveal that approximately 20 % of Chinese regions are undergoing a decrease in ecosystem resilience, with over half experiencing stressed conditions. This is particularly pronounced in the northeastern and central regions of China, where a more significant and widespread decrease in resilience is observed. In the stressed areas of China, the effect of each environmental factor on the decrease in resilience is greater, where attention needs to be paid to areas with higher maximum temperature, precipitation, vapor pressure deficit, and radiation. The study highlights the variability in ecosystem resilience under different environmental conditions and their varied responses to environmental changes. This provides a scientific basis for protecting ecological balance and promoting the sustainable development of ecosystems.

Farmland change at different altitudes: A global analysis of climate and anthropogenic influences

With intensifying climate change and anthropogenic activities, the spatial distribution and productivity of global farmland are undergoing significant shifts. Comprehensive understanding of these changes across different altitudinal gradients remains limited. This study aims to analyze regions of significant farmland change from 1981 to 2015, focusing on how climate change and anthropogenic activities influence these changes across different altitudes. The study utilizes Ordinary Least Squares regression and Mann-Kendall tests to identify significant farmland changes. Residual analysis is used to pinpoint areas where climate change is the predominant factor, while partial correlation analysis explores the climatic factors influencing farmland across various altitudes. Significant changes in global farmland are primarily concentrated within the 0-200 m altitude range, with significantly greening areas outweighing browning areas across most months and altitudinal ranges. Below 1200 m, anthropogenic activities have a greater impact on farmland changes, whereas above 1200 m, climate change exerts a more pronounced influence. Specific climatic factors such as temperature, precipitation, sunshine duration, and soil moisture show significant variations in their effects on farmland changes across different altitudes and months. This study offers a scientific basis for developing agricultural management and climate adaptation policies.

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.

Tracking Sustainable Restoration in Agro-Pastoral Ecotone of Northwest China

Large-scale ecological restoration (ER) projects have been implemented in northwest China in recent decades as a means to prevent desertification and improve ecosystem services. However, previous studies have demonstrated adverse impacts in the form of widespread soil water deficit caused by intensive ER activities. Understanding the role of climate change and ER efforts in vegetation dynamics and soil moisture consumption is essential for sustainable ecosystem management. Here, we used the break for additive season and trend (BFAST) method to analyse spatial patterns in the normalized difference vegetation index (NDVI) variation over the agro-pastoral ecotone of northwest China (APENC) for 2000–2015. From the combined use of generalized additive modelling (GAM) and residual-trend analysis (RESTREND), we distinguished and quantified the effects of climate and human management on vegetation and soil water dynamics. Approximately 78% of the area showed vegetation variations representing a significant change in NDVI, of which more than 68% were categorized as abrupt changes. Large areas of the abrupt change type, interrupted increase and monotonic increase in NDVI were observed before 2006, and small areas of the change type of negative reversals were observed after 2012. Anthropogenic activity was found to be the major driving factor of variation in vegetation (contribution rate of 56%) and soil moisture (contribution rate of 78%). The vegetation expansion, which was mainly related to the large number of ER programs that started in 2000, was found to increase soil moisture depletion. By comparing areas where anthropogenic activities had a high contribution rate to vegetation increase and areas where soil moisture consumption was severely increased, we identify and discuss hotspot areas of soil moisture consumption caused by the ER programs. The current methodological workflow and results represent a novel foundation to inform and support water resource management and ecological-restoration-related policy making.