The topographic differentiation patterns of changes in habitat quality are of great significance for the scientific formulation of environmental protection policies in mountainous areas. Here, the distribution, changing trends, and the effects of the topographic gradient on habitat quality were studied using the InVEST model, the topographic distribution index, and the Mann–Kendall test. The results showed that at p < 0.05 (Z = 1.67), the habitat quality from 2000 to 2020 showed three types of trends (significant decline, non-significant change, and significant increase), accounting for 22.2%, 41.8%, and 36% of the changes, respectively. Because of the livelihood structure of the local residents and geological disasters in high-elevation areas, this terrain was the predominant area showing a significant decline in habitat quality. Thanks to the consolidation of projects for the protection of natural forest resources, the return of farmland to forest, and the implementation of projects for protecting the natural forest, the low-lying topography was the predominant area showing a significant increase in habitat quality. The middle topographic position was the predominant area showing no significant changes in habitat quality. Based on the results of the analysis, ecological management and protection measures for high-, medium-, and low-elevation areas were suggested.

Remote sensing is widely used for lake-water-quality monitoring, but the inversion of the total nitrogen (TN) and total phosphorus (TP) of rivers and non-optical parameters is still a difficult problem. The use of high spatial and temporal resolution multispectral imagery combined with machine learning techniques is an effective solution for this difficulty. Three machine learning methods based on support vector regression (SVR), neural network (NN) and random forest (RF) were used to invert TN and TP using actual water-quality measurement data and Sentine-2 remote-sensing images, and analyzed the factors influencing water quality in terms of pollutant emissions and land use. The results show that RF performs the best in both TN (R² = 0.800, RMSE = 0.640, MSE = 0.400, MAE = 0.480) and TP (R² = 0.830, RMSE = 0.033, MSE = 0.001, MAE = 0.022) inversion models, and that the optimal selection of feature variables improves model performance. The TN and TP concentrations in the Minjiang River Meishan Water Function Development Zone were the highest in the downstream section and in 2018. Analysis of the factors influencing water quality shows that pollution sources and amounts were closely related to land-use types, and land use in riparian zones at different spatial scales had different degrees of impact on water quality.

The precise positioning of dynamic and static objects such as vehicles and pedestrians is a key technology. A global navigation satellite system signal is the primary signal source required to achieve precise positioning, and the optimal estimation method used in precise positioning is Kalman filtering (KF). Standard KF can only achieve optimal estimation results under the conditions that the mathematical model has been determined and the noise characteristics are known. However, when there are measurement outliers and prior noise information deviations, the accuracy of KF cannot be guaranteed. To effectively solve the above problems, a combined adaptive robust Kalman filter (CARKF) algorithm is proposed. First, the influence of measurement outliers on KF accuracy is resisted using the robust Kalman filter method. Then, the influence of an anomalous innovation on its autocorrelation sequence and cross-correlation sequence is resisted by the robust estimator for correlated observations based on the IGGIII method, to ensure the correct estimation of the correlation function sequence of the filtered innovation statistics. Finally, the autocovariance least-squares method is used to eliminate the coupling effect between noise covariances and accurately estimate the unknown noise covariance information. Moreover, an iterative strategy is adopted to eliminate the effect of errors caused by prior noise covariance deviations. The results of the three experiments show that the CARKF method can not only effectively resist the influence of measurement outliers on filtering accuracy and noise covariance valuation but also overcome the influence of errors caused by prior noise deviations and accurately simultaneously estimate two kinds of unknown noise covariance information.

Water Resource Carrying Capacity (WRCC) serves as a critical indicator for assessing the supportive capacity of water resource systems. This study selects 28 districts and counties within the Dadu River Basin as case studies, constructs a WRCC evaluation framework based on the four-dimensional collaborative perspective of “water resources–society–economy–ecology,” proposes a combined weighting method integrating the CRITIC and entropy weight approaches to optimize index weight calculation, and applies the obstacle degree model to investigate the spatio-temporal heterogeneity of regional WRCC and its underlying driving mechanisms. The results show the following: (1) From 2011 to 2020, the WRCC in the Dadu River Basin increased by 17% as a whole. All districts and counties showed an upward trend. (2) The spatial differentiation of WRCC is significant. The downstream regions are approaching the fourth-level threshold, driven by the adoption of water-saving technologies and the agglomeration effects of economic activities. In contrast, the upstream regions face inherent constraints arising from the conflict between ecological conservation and resource exploitation, leading to a relatively slower growth rate. (3) The driving mechanism of WRCC shows the transformation characteristics of “shifting from being dominated by the social economy to the synergy of the economy and ecology”. Based on this analysis, a strategy of “zonal regulation–structural optimization–management upgrade” is proposed.

The Kalman smoothing algorithm is widely used in offline data processing in global navigation satellites system (GNSS) to improve filter calculation accuracy. When there are measurement outliers or biased random models, the adaptive Kalman smoothing algorithm can reduce their impact on the smoothing results to a certain extent. Nevertheless, the adaptive Kalman smoothing algorithm usually has difficulty obtaining optimal estimation in a complex observation environment due to the inherent defects of the algorithm itself. Therefore, we propose an adaptive robust least-squares smoothing algorithm. The row and column transformation of the least-square smooth noise covariance matrix is realized by constructing an orthogonal space transformation matrix, and the noise covariance estimation problem of the Kalman filter is transformed into the problem of solving the variance–covariance component elements under a specific matrix structure. In addition, by selecting the observation value with the largest standardized residual to determine whether it is a gross error, we avoid the gross error misjudgment problem caused by the two-factor robust estimation method under least-square smoothing and further improve the smoothing accuracy. Finally, two experiments are studied to compare the performance of the new algorithm and the state-of-the-art algorithms. The proposed algorithm has demonstrated better performance in terms of robustness and estimation accuracy.