This study was aimed at optimizing the weighted linear combination method (WLC) for agricultural land suitability evaluation (ALSE) through indicator selection, weight determination, and classification of overall suitability scores in Handan, China. Handan is a representative research area with distinct agricultural advantages and regional differences in land use, where the expansion of construction land has led to a rapid decrease of agricultural land in recent years. Natural factors (topography, climate, soil conditions, and vegetation cover) and socioeconomic factors (land use and spatial accessibility) were selected to establish a more comprehensive evaluation system. The index weight was calculated by the mutual information between index suitability and current land use. The consistency index was used to identify the boundary value dividing the overall suitability score into a suitable category and unsuitable category in each sub-region. The results demonstrated that the optimized WLC-ALSE model outperformed the comparison models using conventional methods in terms of the consistency between the evaluation results and current land use. Owing to the increasing limitations of topography, soil conditions, spatial accessibility, and land use, the proportions of suitable land in Zone 1, Zone 2, and Zone 3 were 77.4%, 67.5%, and 30.9%, respectively. The agricultural land unsuitable for agriculture (14.5%) was less than non-agricultural land suitable for agriculture (7.4%), indicating that agricultural land had low growth potential in Handan. Finally, specific recommendations were made to improve agricultural land suitability, alleviate land use conflicts, and further optimize the model. The results can provide effective guidance for WLC-ALSE and land use decision-making for sustainable agriculture.

Long time series land cover classification information is the basis for scientific research on urban sprawls, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the random forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of the sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of the sample points without land class change, determined by counting the sample points of each band of the Landsat time series from 1986 to 2022, was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of the TM and ETM+ sensor data from 2013 to 2022; and (iii) the addition of a mining land cover type increases the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and forest area. Among the land classifications via multi-source remote sensing, the combined variables of Spectral band + Index + Terrain + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and the use of sensors under complex terrain conditions. The use of the GEE cloud computing platform enabled the rapid analysis of remotely sensed data to produce land cover maps with high accuracy and a long time series.

The inter-annual Normalized Difference Vegetation Index (NDVI) time series derived from Landsat (INSL) has been applied to many fields. However, INSL may contain noise from many factors. At present, most researchers focus on denoising for NDVI time series derived from other datasets with shorter time intervals. In this paper, we proposed a denoising method for INSL by combining the best index slope extraction algorithm (BISE) with wavelet transform (WT). This method consists of two steps: (a) INSL are processed by BISE under a limiting condition to remove the significant noise; and (b) INSL obtained by step (a) are filtered by WT. Commonly used methods, including Savitzky-Golay filter (S-G) and BISE, were used as references to evaluate the proposed method. Using qualitative and quantitative comparisons and a change detection analysis, the proposed method was demonstrated to be reliable.

Mining disturbances affect the environmental quality, local ecology and human populations. Thus, a lot of public attention has been paid on mining issues and the need for the detection of mining disturbance. Research on mining disturbance identification is mainly for forested regions where other disturbances account for a very small areal proportion. It is more challenging to identify mining disturbance of arid or semi-arid steppe due to its vulnerable ecology and existence of other disturbances. In this letter, we proposed a method to identify mining disturbance for arid or semi-arid steppe using inter-annual Landsat images. The method utilizes both spectral characteristics and topology information. We applied the method to Bal Rishi Le mining area located in north-eastern China. By accuracy assessment, the method was proven to be reliable with 92.2% overall accuracy.