General Landscape Connectivity Model (GLCM): a new way to map whole of landscape biodiversity functional connectivity for operational planning and reporting

Estimating soil profile salinity under vegetation cover based on UAV multi-source remote sensing

Soil salinization is the most prevalent form of land degradation in arid, semi-arid, and coastal regions of China, posing significant challenges to local crop yield, economic development, and environmental sustainability. However, limited research exists on estimating soil salinity at different depths under vegetation cover. This study employed field-controlled soil experiments to collect multi-source remote sensing data on soil salt content (SSC) at varying depths beneath barley growth. Three types of feature variables were derived from the images and filtered using the boosting decision tree (BDT) method. In addition, four machine learning algorithms coupled with seven variable combination groups were applied to establish comprehensively soil salinity estimation models. The performances of estimation model for different crop coverage ratios and soil depth were then evaluated. The results showed that the gaussian process regression (GPR) model, based on the whole variable group for depths of 0 ~ 10 cm and 30 ~ 40 cm, outperformed other models, achieving validation R2 values of 0.774 and 0.705, with RMSE values are 0.185% and 0.31%, respectively. For depths of 10 ~ 20 cm and 20 ~ 30 cm, the random forest (RF) models, incorporating spectral index and texture data, demonstrated superior accuracy with R2 values of 0.666 and 0.714. The study confirms that SSC can be quantitatively estimated at various depths using the machine learning model based on multi-source remote sensing, providing a valuable approach for monitoring soil salinization.

Improving the ecological network optimization with landscape connectivity: a case study of Neijiang City, Sichuan Province

Rapid urbanization intensifies the fragmentation of landscape patches and affects the stability of ecosystems. The construction of an ecological network can effectively promote the connection of important ecological spaces and improve the landscape integrity. However, the landscape connectivity, directly affecting the stability of ecological network, was less considered in the ecological network construction of recent researches, which easily caused the instability of constructed ecological network. Therefore, this study introduced landscape connectivity index to establish a modified ecological network optimization method based on the minimum cumulative resistance (MCR) model. The results showed that, compared with the traditional model, the modified model focused on the spatially detailed measurement of regional connectivity, and emphasized the impact of human disturbance on ecosystem stability at the landscape scale. The constructed corridors in the optimized ecological network of the modified model not only effectively improved the connection degree between important ecological sources but also avoided the areas with low landscape connectivity and high obstacles to ecological flow, especially in the counties of Zizhong, Dongxing, and Longchang within the focal study area. The ecological network established by the traditional model and modified model generated 19 and 20 ecological corridors with lengths of 334.49 km and 364.35 km, respectively, and the number of ecological nodes was 18 and 22. Evaluated by the Gravity method, the modified model identified the important ecological corridors in the ecological network, and the energy transfer efficiency of the network was improved. This study provided an effective way to improve the structural stability of ecological network construction and can provide scientific support for regional landscape pattern optimization and ecological security construction.