As a geological disaster widely distributed in the southern regions of China, rainfall-induced shallow landslides pose a significant threat to affected areas. Timely detection of landslides is crucial in the effective response to such disasters. However, landslide detection faces adverse impacts from various factors, such as insufficient sample data, complex model structures, and limitations in detection accuracy during the actual detection process. In this study, high-quality image samples were collected from multiple landslide disaster areas in southern China, and a rainfall-induced shallow landslide sample database was constructed in the region. Based on this, a lightweight attention-guided YOLO model (LA-YOLO) was proposed to improve the detection performance of YOLO model for rainfall-induced shallow landslides. First, CG block is introduced to enhance the C2f module, enriching the feature representation capability through multiscale feature fusion and reducing the model’s parameters and computational complexity. Second, the SimAM attention module is used to focus on the target regions, improving feature extraction effectiveness. Experimental results show that the model parameters of LA-YOLO were reduced by approximately 30%, with precision, recall, and mean average precision (mAP) on the landslide sample dataset increasing by 2.6%, 0.7%, and 2.2%, respectively. While ensuring model detection performance, the model structure was significantly optimized, achieving both lightweight and accuracy goals, confirming the model’s superiority in monitoring rainfall-induced shallow landslide disasters.

Leaf water content (LWC) is very important in the growth of vegetation. LWC and leaf spectra change when the leaves are under pest stress; exploring the change mechanism between LWC, leaf spectra, and pest stress can lay the foundation for pest detection. In this study, we measured the LWC and leaf spectra of moso bamboo leaves under different damage levels, used the Pearson–Lasso method to screen the features, and established a multiple linear regression (MLR) and random forest regression (RFR) model to estimate the LWC. We analyzed the relationship between LWC and spectral features of moso bamboo leaves under Pantana phyllostachysae Chao (PPC) stress and their changes. The results showed that: (1) the LWC showed a decreasing trend as the pest level increased. (2) The spectra changed substantially when the leaves were under pest stress. (3) The number and significance of response features associated with the LWC were diverse under different damage levels. (4) The estimation of LWC under different damage levels differed significantly. LWC, leaf spectra, response features, and the model estimation effect were diverse under different damage levels. The correlation between LWC and features was higher for healthy leaves than for damaged and off-year leaves. The two models were more effective in estimating the LWC of healthy leaves but less effective for damaged and off-year leaves. This study provides theoretical support for the prediction of PPC stress and lays the foundation for remote sensing monitoring.

The Russia-Ukraine conflict has persisted for over a year, posing challenges in assessing and verifying the extent of damage through on-site investigations. Nighttime light (NTL) remote sensing, an emerging approach for studying regional conflicts, can complement traditional methods. This study employs NASA's Black Marble products to reveal the response characteristics of NTL intensity at national and state scales during the first anniversary of the conflict (January 2022 to February 2023) in Ukraine. The study used the nighttime light ratio index (NLRI) to assess the relative intensity of NTL and month-on-month change rate (MoM), nighttime light change rate index (NLCRI), and the rate (R value) of linear regression analysis to depict spatiotemporal dynamics. In addition, Theil-Sen median trend analysis and Mann-Kendall tests were employed to analyze intensity trends, with a “dual-threshold method” to reduce extensive noise interference. The results showed: At the national scale, the conflict resulted in an 84.0% decrease in NTL across Ukraine. At the state scale, the most severe NTL decline occurred near the southwestern border and eastern conflict zone under Ukrainian government control, witnessing over 80% decline rates. The correlation of decreases in NLCRI and R values with population displacement, infrastructure damage, or curfew measures demonstrated that the concentration of refugees and electricity facility restoration led to increased NLCRI and R values. Overall, NTL reflects critical moments at the national scale and provides insights into military intentions and humanitarian measures at the state scale. Therefore, NTL can effectively serve as a tool for observation and assessment in military conflicts.

To control the negative effects resulting from the disorderly development of aquaculture ponds and promote the development of the aquaculture industry, rapid and accurate identification and extraction techniques are essential. An aquaculture pond is a special net-like water body divided by complex roads and dikes. Simple spectral features or spatial texture features are not sufficient to accurately extract it, and the mixed feature rule set is more demanding on computer performance. Supported by the GEE platform, and using the Landsat satellite data set and corresponding DEM combined with field survey data, we constructed a decision-making model for the extraction of aquaculture ponds in the coastal waters, and applied this method to the coastal waters of Southeast China. This method combined the image spectral information, spatial features, and morphological operations. The results showed that the total accuracy of this method was 93%, and the Kappa coefficient was 0.86. The overlapping proportions of results between the automated extraction and visual interpretation for test areas were all more than 90%, and the average was 92.5%, which reflected the high precision and reliability of this extraction method. Furthermore, in 2020, the total area of coastal aquaculture ponds in the study area was 6348.51 km², which was distributed primarily in the cities of Guangdong and Jiangsu. Kernel density analysis suggested that aquaculture ponds in Guangdong and Jiangsu had the highest degree of concentration, which means that they face higher regulatory pressure in the management of aquaculture ponds than other provinces. Therefore, this method can be used to extract aquaculture ponds in coastal waters of the world, and holds great significance to promote the orderly management and scientific development of fishery aquaculture.

Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the most important sensors onboard the Terra and Aqua satellites and can be used in observing the air, the sea and the land quickly, timely and multi-temporally, which makes it an attractive and powerful tool for ecosystem pattern and process study. Carnegie-Ames-Stanford Approach (CASA) model is one of the most quick, convenient and accurate models to estimate the NPP (Net Primary Productivity) of vegetation. In this paper, the estimation and analysis of spatial and temporal distribution of vegetation NPP of the Haihe River Basin, the most man-interfered area in China, was made by using the CASA model based on the data of MODIS sensor onboard on the Terra satellites from the beginning of 2000 (the first year of delivering data) to the end of 2006. The result shows that the total NPP of the study area ranged from 1.25 × 10¹⁴ to 1.54 × 10¹⁴ gC/a during 2000 to 2006. The crop yields measured in the study area and the NPP value of the agriculture area from the CASA model were compared for testing the model's applicability in this study. The testing result shows that the NPP forecasted by using MODIS data and CASA model is acceptable. The NPP of the study area in all the years accumulated mainly from May to September, in which the maximum NPP appears in July and in August. The average NPP of the seven years of the main four vegetation types varied differently: values of two ripe crops a year or three ripe crops two years (519 gC/m²•a) > value of temperate deciduous shrub (428 gC/m²•a) > value of crop of one ripe a year and value of cold tolerating economic crop (293 gC/m²•a) > value of temperate grassland (195 gC/m²•a). From the results of the time series, it shows that the NPP varied irregularly with the time, but the NPP of all vegetation types in 2001 is lower than those in any other years. It relates to the drought climate in 2000 and 2001.