Asia, Europe, and Africa, connected by the maritime silk road (MSR) and the land silk road (LSR), as the cradle of ancient civilizations, contain rich cultural heritage resources. While aiming to achieve differentiated and targeted application, protection, and development of world cultural heritage (WCHS), it is essential to identify the temporal-spatial distribution features of WCHS on these three continents. For these WCHS elements, based on big earth data, we semi-quantitatively describe the features of spatial-temporal distribution, material types, civilization and religion characters, capital investment capacity, and risks by data cleaning, spatial analysis, and risk assessment. Our findings reveal features in these WCHS elements, including temporal and spatial distribution, the amount of different ancient civilizations and cultural diversity, and the need for preservation and funding capacity. Finally, this study’s results indicate that we should take full advantage of the potential of cultural heritage, digital technologies, and the scientific protection of cultural heritage along with the MSR and LSR. Moreover, declarations of heritage in Africa and Central Asia should be extended to realize the precise “poverty alleviation” of WCHS protection in these regions. For further planning of WCHS sites along the Silk Road, we propose countermeasures to protect the global cultural heritage in the future and also provide theoretical guidance and specific scientific implementation directions to “strengthen efforts to protect and safeguard the world’s cultural and natural heritage,” as proposed by the United Nations.

Wheat stripe rust poses a serious threat to wheat production. An effective prediction method is important for food security. In this study, we developed a prediction model for wheat stripe rust based on vegetation indices and meteorological features. First, based on time-series Sentinel-2 remote sensing images and meteorological data, wheat phenology (jointing date) was estimated using the harmonic analysis of time-series combined with average cumulative temperature. Then, vegetation indices were extracted based on phenological information. Meteorological features were screened using correlation analysis combined with independent t-test analysis. Finally, a random forest (RF) was used to construct a prediction model for wheat stripe rust. The results showed that the RF model using the input combination (phenological information-based vegetation indices and meteorological features) produced a higher prediction accuracy and a kappa coefficient of 88.7% and 0.772, respectively. The prediction model using phenological information-based vegetation indices outperformed the prediction model using single-date image-based vegetation indices, and the overall accuracy improved from 62.9% to 78.4%. These results indicated that the method combining phenological information-based vegetation indices and meteorological features can be used for wheat stripe rust prediction. The results of the prediction model can provide guidance and suggestions for disease prevention in the study area.

Desert locust plagues can easily cause a regional food crisis and thus affect social stability. Preventive control of the disaster highlights the early detection of hopper gregarization before they form devastating swarms. However, the response of hopper band emergence to environmental fluctuation exhibits a time lag. To realize the dynamic forecast of band occurrence with optimal temporal predictors, we proposed an SVM-based model with a temporal sliding window technique by coupling multisource time-series imagery with historical locust ground survey observations from between 2000–2020. The sliding window method was based on a lagging variable importance ranking used to analyze the temporal organization of environmental indicators in band-forming sequences and eventually facilitate the early prediction of band emergence. Statistical results show that hopper bands are more likely to occur within 41–64 days after increased rainfall; soil moisture dynamics increasing by approximately 0.05 m³/m³ then decreasing may enhance the chance of observing bands after 73–80 days. While sparse vegetation areas with NDVI increasing from 0.18 to 0.25 tend to witness bands after 17–40 days. The forecast model combining the optimal time lags of these dynamic indicators with other static indicators allows for a 16-day extended outlook of band presence in Somalia, Ethiopia, and Kenya. Monthly predictions from February to December 2020 display an overall accuracy of 77.46%, with an average ROC-AUC of 0.767 and a mean F-score close to 0.772. The multivariate forecast framework based on the lagging effect can realize the early warning of band presence in different spatiotemporal scenarios, supporting early decisions and response strategies for desert locust preventive management.

The Oriental migratory locust is a destructive agricultural pest in China. Large-scale locust area (the area possessing suitable breeding habitat for locusts and has locust infestation) extraction and its evolution analysis are essential for locust ecological control. Existing methods seldom consider the spatial differences in the locust development and habitat landscape structures in large areas. To analyze these effects, our study proposed a landscape-based habitat suitability model (LHS model) for large-scale locust area extraction based on remote sensing data, taking the middle and lower reaches of the Yellow River (MLYR) as an example. Firstly, the DD model was used to simulate locust development and obtain habitat factors of the corresponding dates; secondly, the patch distribution of different land cover classes and their adjacent landscape characteristics were analyzed to determine the landscape-based factors memberships; finally, the habitat suitability index was calculated by combining the factors memberships and weights to extract the locust area. Compared with the patch-based model using moving windows (patch based-analytic hierarchy process model, R² = 0.77), the LHS model accuracy improved significantly (R² = 0.83). Our results showed that the LHS model has a better application prospect in large-scale locust area extraction. By analyzing the locust areas evolution along the MLYR extracted using the LHS model, we found human activities were the main factors affecting the locust areas evolution from 2016 to 2020, including: (1) planting the plants that locusts do not like and urbanization caused the decrease of the locust area; (2) the wetland protection policies may cause the increase of the locust area. The model and research results help locust control and prevention to realize the sustainable development of agriculture.