Search Results (4)

The Lower Liaohe River Plain (LLRP) is a core grain production base in Northeast China. Monitoring the dynamics and changing rates of soil organic carbon (SOC) in cultivated lands is essential for regulating soil fertility, safeguarding food production, and maintaining the regional carbon balance. Based on soil survey data from three periods, 1980, 2008, and 2019, this study investigated the spatiotemporal dynamics of SOC content and its changing rate (SOC_r) using geospatial analysis. Results showed that SOC content declined significantly from 11.19 g kg⁻¹ to 10.47 g kg⁻¹ during 1980–2008, then recovered slightly to 10.58 g kg⁻¹ in 2019. Moreover, SOC_r varied temporally in the period of 2008–2019, exhibiting a positive mean rate of 0.01 g kg⁻¹ yr⁻¹, which was significantly higher than that of the period of 1980–2008 (−0.03 g kg⁻¹ yr⁻¹). A significant negative correlation was examined between the initial SOC content and SOC_r, showing an identification of the SOC equilibrium point (SOC_ep). The SOC_ep in the period of 2008–2019 was 9.69% higher than that in the period of 1980–2008. These findings provide a scientific basis for formulating regional policies and optimizing spatially differentiated management strategies to improve cropland SOC in the study area. Full article

With accelerating urbanization and agricultural modernization, the scale, structure, and land use conditions of rural settlements in China’s three northeastern provinces (TNPs) have changed dramatically, impacting regional food production and sustainable rural development. Based on multitemporal land use datasets and socioeconomic statistics, we used spatial pattern analysis, machine learning models, and the Shapley additive explanation (SHAP) method to investigate the spatial evolutionary characteristics and driving factors of rural settlements in China’s TNPs from 1980 to 2020. The results show that (1) the spatial evolution of rural settlements followed a four-stage “expansion–stabilization–re-expansion–restabilization” trend; arable land conversion was the primary source of expansion, with limited conversion from forests, grasslands, and water bodies. (2) Rural settlements demonstrated marked agglomeration, with the spatial distribution evolving from “single-center clustering” to “multiregional contiguous clustering”. Rural settlements in the Sanjiang Plain evolved into large patch clusters, while those in the lower Liaohe River Basin became small patch clusters. (3) Rural settlements at low elevations and near roads and waterways presented a large-scale, agglomerative distribution, while settlements at high elevations and far from rivers and roads showed a small-scale, high-agglomeration pattern. (4) The rural population, total power of agricultural machinery, total grain output, and primary industry value added predominantly drove settlement spatial expansion, with an “initial suppression, then promotion” trend, while the urbanization rate and GDP per capita had a negative impact, with the opposite trend. The interaction effects among high-contributing factors transitioned from suppressive to promoting. Our results provide theoretical insights for spatial planning and sustainable development in agricultural rural settlements. Full article

Understanding and managing land use/cover changes (LUCC) is crucial for ensuring the sustainability of the region. With the support of remote sensing technology, intensity analysis, the geodetic detector model, and the Mixed-Cell Cellular Automata (MCCA) model, this paper constructs an integrated framework linking historical evolutionary pattern-driving mechanisms for future simulation for LUCC in the Lower Liaohe Plain. From 1980 to 2018, the increasing trends were in built-up land and water bodies, and the decreasing trends were in grassland, cropland, forest land, unused land, and swamps. Overall, the changes in cropland, forest land, and built-up land are more active, while the changes in water bodies are more stable; the sources and directions of land use conversion are more fixed. Land use changes in the Lower Liaohe Plain are mainly influenced by socio-economic factors, of which population density, primary industry output value, and Gross Domestic Product (GDP) have a higher explanatory power. The interactive influence of each factor is greater than any single factor. The results of the MCCA model showed high accuracy, with an overall accuracy of 0.8242, relative entropy (RE) of 0.1846, and mixed-cell figure of merit (mcFoM) of 0.1204. By 2035, the built-up land and water bodies will increase, while the rest of the land use categories will decrease. The decrease is more pronounced in the central part of the plains. The findings of the study provide a scientific basis for strategically allocating regional land resources, which has significant implications for land use research in similar regions. Full article

It is an important issue to explore achieving high accuracy long-term crop classification with limited historical samples. The West Liaohe River Basin (WLRB) serves as a vital agro-pastoral ecotone of Northern China, which experiences significant changes in crop planting structure due to a range of policy. Taking WLRB as a case study, this study constructed multidimensional features for crop classification suitable for Google Earth Engine cloud platform and proposed a method to extract main grain crops using sample augmentation and model migration in case of limited samples. With limited samples in 2017, the method was employed to train and classify crops (maize, soybean, and rice) in other years, and the spatiotemporal changes in the crop planting structure in WLRB from 2014 to 2020 were analyzed. The following conclusions were drawn: (1) Integrating multidimensional features could discriminate subtle differences, and feature optimization could ensure the accuracy and efficiency of classification. (2) By augmenting the original sample size by calculating the similarity of the time series NDVI (normalized difference vegetation index) curves, migrating the random forest model, and reselecting the samples for other years based on the model accuracy scores, it was possible to achieve a high crop classification accuracy with limited samples. (3) The main grain crops in the WLRB were primarily distributed in the northeastern and southern plains with lower elevations. Maize was the most predominant crop type with a wide distribution. The planting area of main grain crops in the WLRB exhibited an increasing trend, and national policies primarily influenced the variations of planting structure in maize and soybean. This study provides a scheme for extracting crop types from limited samples with high accuracy and can be applied for long-term crop monitoring and change analysis to support crop structure adjustment and food security. Full article