Assessment of suitable areas for afforestation and its carbon sink value in fragile ecological areas of northern China.

Afforestation and reforestation are pivotal in mitigating land degradation and bolstering the carbon sink capacity of terrestrial ecosystems. However, the potential economic ramifications of afforestation and reforestation in the context of climate change remain largely unexplored. In this study, we employed an interdisciplinary methodology to establish a framework for assessing future forest potential and carbon sequestration in the Eastern Loess Plateau region of China. Our findings indicate that an estimated 17,392.99 km2 of land suitable for afforestation still existed within the region, exhibiting a propensity to aggregate around existing forests rather than being dispersed randomly. Notably, 4385.36 km2 was prioritized for afforestation initiatives. Projections suggest a significant enhancement of the forest carbon sink within the study area by 2050, ranging from 36.93 Mt to 105.38 Mt. The corresponding economic value for this enhancement is estimated to vary between US$3.25 billion and US$17.68 billion. Of significance is the observed polarization of the region's carbon sink capacity over time, with half of the total carbon sinks concentrated within 10% of the districts. Additionally, approximately 26% of the counties are expected to transition from carbon sinks to carbon sources. These findings underscore the substantial impact of climate change on forest distribution and suggest a targeted approach to combat forest degradation by circumventing ineffective afforestation activities.

Improved estimation of aboveground biomass of regional coniferous forests integrating UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries.

BACKGROUND: Forest aboveground biomass (AGB) is not only the basis for estimating forest carbon storage, but also an important parameter for evaluating forest carbon cycle contribution and forest ecological function. Data saturation and fewer field plots limit the accuracy of AGB estimation. In response to these questions, we constructed a point-line-polygon framework for regional coniferous forests AGB mapping using field survey data, UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries in this study. Under this framework, we explored the feasibility of acquiring the LiDAR sampling plots using the LiDAR sampling strategy consistent with the field survey, and analyzed the potentials of multi-scale wavelet transform (WT) textures and tree species stratification for improving AGB estimation accuracy of coniferous forests in North China. RESULTS: The results showed that UAV-LiDAR strip data of high density point clouds could be used as a sampling tool to achieve sample amplification. Experimental comparison results showed that the Sentinel-based AGB estimation models incorporating the multi-scale WT textures and SAR data performed better, and the model based on coniferous forests tree species significantly improved the performance of AGB estimation. Additionally, the accuracy comparison using different validation sets indicated that the proposed LiDAR sampling strategy under the point-line-polygon framework was suitable for estimating coniferous forests AGB on a large area. The highest accuracy of AGB estimation of larch, Chinese pine and all coniferous forests was 74.55%, 78.96%, and 73.42%, respectively. CONCLUSIONS: The proposed approach can successfully alleviate the data signal saturation issue and accurately produce a large-scale wall-to-wall high-resolution AGB map by integrating optical and SAR data with a relative small number of field plots.