Understanding the impacts and extent of both climate change and human activities on ecosystems is crucial to sustainable development. With low anti-interference ability, arid and semi-arid ecosystems are particularly sensitive to disturbances from both climate change and human activities. We investigated how and to what extent climate variation and human activities influenced major indicators that are related to ecosystem functions and conditions in the past decades in Xinjiang, a typical arid and semi-arid region in China. We analyzed the changing trends of evapotranspiration (ET), gross primary productivity (GPP) and leaf area index (LAI) derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite product and the Breathing Earth System Simulator (BESS) model in Xinjiang for different climate zones. We separated and quantified the contributions of climate forcing and human activities on the trends of the studied ecosystem indicators using the residual analysis method for different climate zones in Xinjiang. The results show that GPP and LAI increased and ET decreased from 2001 to 2015 in Xinjiang. Factors that dominate the changes in ecosystem indicators vary considerably across different climate zones. Precipitation plays a positive role in impacting vegetation indicators in arid and hyper-arid zones and temperature has a negative correlation with both GPP and LAI in hyper-arid zones in Xinjiang. Results based on residual analysis indicate that human activities could account for over 72% of variation in the changes in each ecosystem indicator. Human activities have large impacts on each vegetation indicator change in hyper-arid and arid zones and their relative contribution has a mean value of 79%. This study quantifies the roles of climate forcing and human activities in the changes in ecosystem indicators across different climate zones, suggesting that human activities largely influence ecosystem processes in the arid and semi-arid regions of Xinjiang in China.
The spatial distribution of cotton fields is primary information for national farm management, the agricultural economy and the textile industry. Therefore, accurate cotton information at the regional scale is required with a rapid increase due to the chance provided by the huge amounts of satellite images accumulated in recent decades. Research has started to introduce the phenology characteristics shown at special growth phases of cotton but frequently focuses on limited vegetation indices with less consideration on the whole growth period. In this paper, we investigated a set of phenological and time-series features with optimization depending on each feature permutation’s importance and redundancy, followed by its performance evaluation through the cotton extraction using the Random Forest (RF) classifier. Three sets of 31 features are involved: (1) phenological features were determined by the biophysical and biochemical characteristics in the spectral space of cotton during each of its five distinctive phenological stages, which were identified from 2307 representative cotton samples using 21,237 Sentinel-2 images; (2) three typical vegetation indices were functionalized into time-series features by harmonic analysis; (3) three terrain factors were derived from the digital elevation model. Our analysis of feature determination revealed that the most valuable discriminators for cotton involve the boll opening stage and harmonic coefficients. Moreover, both qualitative and quantitative validation were performed to evaluate the retrieval of the optimized features-based cotton information. Visual examination of the map exhibited high spatial consistency and accurate delineation of the cotton field. Quantitative comparison indicates that classification of RF-coupled optimized features achieves improved overall accuracy 5.53% higher than that which works with either the limited vegetation indices. Compared with all 31 features, the optimized features realized greater identification accuracy while using only about half the number of features. Compared with test samples, the cotton map achieved an overall accuracy greater than 98% and a kappa more than 0.96. Further comparison of the cotton map area at the county-level showed a high level of consistency with the National Bureau of Statistics data from 2020, with R2 over 0.96, RMSE no more than 14.62 Kha and RRMSE less than 17.78%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.