The urgent need to develop a new grassland map in China: based on the consistency and accuracy of ten land cover products

Evaluation of land ecological security and driving factors in the Lower Yellow River Flood Plain based on quality, structure and function

Land ecological security (LES) is crucial for human well-being and sustainable development, especially in areas like the Lower Yellow River Flood Plain (LYRFP), which faces flood threats, economic challenges, and ecological fragility. This study introduces a "Quality-Structure-Function" framework for evaluating LYRFP's LES, incorporating ecological baselines and the impacts of land use changes on human well-being for a comprehensive assessment. Using the Optimal Parameter Geographic Detector (OPGD) model, we analyzed agricultural, industrial, and socio-economic factors as potential LES drivers. The findings indicate a gradual improvement in LES over the past two decades, with spatial variations-higher in upstream and estuarine areas and lower in the middle. Significant enhancements post-2010 were observed in Shandong Province, unlike the modest gains in Henan. Spatial heterogeneity in LES was evident across floodplain segments, with Jitai Beach witnessing the most decline, Dongying Beach the most improvement, and Zhengkai Beach the largest internal disparities. Economic growth and reduced agricultural activities positively impacted LES, while population growth-related human activities contributed to its decline. This study suggested land use safety improvements in LYRFP by considering spatiotemporal and influencing factors for regional ecological protection and development.

Validation and refinement of cropland map in southwestern China by harnessing ten contemporary datasets

Accurate cropland map serves as the cornerstone of effective agricultural monitoring. Despite the continuous enrichment of remotely sensed cropland maps, pervasive inconsistencies have impeded their further application. This issue is particularly evident in areas with limited valid observations, such as southwestern China, which is characterized by its complex topography and fragmented parcels. In this study, we constructed multi-sourced samples independent of the data producers, taking advantage of open-source validation datasets and sampling to rectify the accuracy of ten contemporary cropland maps in southwestern China, decoded their inconsistencies, and generated a refined cropland map (CroplandSyn) by leveraging ten state-of-the-art remotely sensed cropland maps released from 2021 onwards using the self-adaptive threshold method. Validations, conducted at both prefecture and county scales, underscored the superiority of the refined cropland map, aligning more closely with national land survey data. The refined cropland map and samples are publicly available to users. Our study offers valuable insights for improving agricultural practices and land management in under-monitored areas by providing high-quality cropland maps and validation datasets.

Impact of greening trends on biogenic volatile organic compound emissions in China from 1985 to 2022: Contributions of afforestation projects

The rapid expansion of green areas in China has enhanced carbon sinks, but it also presents challenges regarding increased biogenic volatile organic compound (BVOC) emissions. This study examines the impact of greening trends on BVOC emissions in China from 1985 to 2001 and from 2001 to 2022, focusing on evaluating long-term trends in BVOC emissions within eight afforestation project areas during these two periods. Emission factors for 62 dominant tree species and provincial Plant Functional Types were updated. The BVOC emission inventories were developed for China at a spatial resolution of 27 km × 27 km using the Model of Emissions of Gases and Aerosols from Nature. The national BVOC emissions in 2018 were estimated at 54.24 Tg, with isoprene, monoterpenes, sesquiterpenes, and other BVOC contributing 26.94 Tg, 2.29 Tg, 0.44 Tg, and 24.57 Tg, respectively. Over the past 37 years, BVOC emissions experienced a slow growth rate of 1.7 % (0.79 Tg) during 1985-2001, followed by a significant increase of 12 % (6 Tg) from 2001 to 2022. BVOC emissions in the eight afforestation project areas increased by 2 % and 20 % during the two periods. From 2001 to 2022, at the regional scale, the Shelterbelt program for the middle reaches of the Yellow River area exhibited the largest rate of increase (43 %) in BVOC emissions. The Shelterbelt program for the upper and middle reaches of the Yangtze River made the most largest contribution (45 %) to the national increase in BVOC emissions. Afforestation projects have shifted towards planting more broadleaf trees than needleleaf trees from 2001 to 2022, and there also showed a change from herbaceous plants to broadleaf trees. These trends have led to higher average emission factors for vegetation, resulting in increased BVOC emissions. It underscores the importance of considering BVOC emissions when evaluating afforestation initiatives, emphasizing the need to balancing ecological benefits with potential atmospheric consequences.

The rectangular tile classification model based on Sentinel integrated images enhances grassland mapping accuracy: A case study in Ordos, China

Arid zone grassland is a crucial component of terrestrial ecosystems and plays a significant role in ecosystem protection and soil erosion prevention. However, accurately mapping grassland spatial information in arid zones presents a great challenge. The accuracy of remote sensing grassland mapping in arid zones is affected by spectral variability caused by the highly diverse landscapes. In this study, we explored the potential of a rectangular tile classification model, constructed using the random forest algorithm and integrated images from Sentinel-1A (synthetic aperture radar imagery) and Sentinel-2 (optical imagery), to enhance the accuracy of grassland mapping in the semiarid to arid regions of Ordos, China. Monthly Sentinel-1A median value images were synthesised, and four MODIS vegetation index mean value curves (NDVI, MSAVI, NDWI and NDBI) were used to determine the optimal synthesis time window for Sentinel-2 images. Seven experimental groups, including 14 experimental schemes based on the rectangular tile classification model and the traditional global classification model, were designed. By applying the rectangular tile classification model and Sentinel-integrated images, we successfully identified and extracted grasslands. The results showed the integration of vegetation index features and texture features improved the accuracy of grassland mapping. The overall accuracy of the Sentinel-integrated images from EXP7-2 was 88.23%, which was higher than the accuracy of the single sensor Sentinel-1A (53.52%) in EXP2-2 and Sentinel-2 (86.53%) in EXP5-2. In all seven experimental groups, the rectangular tile classification model was found to improve overall accuracy (OA) by 1.20% to 13.99% compared to the traditional global classification model. This paper presents novel perspectives and guidance for improving the accuracy of remote sensing mapping for land cover classification in arid zones with highly diverse landscapes. The study presents a flexible and scalable model within the Google Earth Engine framework, which can be readily customized and implemented in various geographical locations and time periods.

Past dynamics and future prediction of the impacts of land use cover change and climate change on landscape ecological risk across the Mongolian plateau

Land use/land cover (LULC) change and climate change are interconnected factors that affect the ecological environment. However, there is a lack of quantification of the impacts of LULC change and climate change on landscape ecological risk under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) on the Mongolian Plateau (MP). To fill this knowledge gap and understand the current and future challenges facing the MP's land ecological system, we conducted an evaluation and prediction of the effects of LULC change and climate change on landscape ecological risk using the landscape loss index model and random forest method, considering eight SSP-RCP coupling scenarios. Firstly, we selected MCD12Q1 as the optimal LULC product for studying landscape changes on the MP, comparing it with four other LULC products. We analyzed the diverging patterns of LULC change over the past two decades and observed significant differences between Mongolia and Inner Mongolia. The latter experienced more intense and extensive LULC change during this period, despite similar climate changes. Secondly, we assessed changes in landscape ecological risk and identified the main drivers of these changes over the past two decades using a landscape index model and random forest method. The highest-risk zone has gradually expanded, with a 30% increase compared to 2001. Lastly, we investigated different characteristics of LULC change under different scenarios by examining future LULC products simulated by the FLUS model. We also simulated the dynamics of landscape ecological risks under these scenarios and proposed an adaptive development strategy to promote sustainable development in the MP. In terms of the impact of climate change on landscape ecological risk, we found that under the same SSP scenario, increasing RCP emission concentrations significantly increased the areas with high landscape ecological risk while decreasing areas with low risk. By integrating quantitative assessments and scenario-based modeling, our study provides valuable insights for informing sustainable land management and policy decisions in the region.