Flooding is one of the most severe natural hazards, causing widespread environmental, economic, and social disruption. If not managed properly, it can lead to human losses, property damage, and the destruction of livelihoods. The ability to rapidly assess such damages is crucial for emergency management. Near Real-Time (NRT) spatial information on flood-affected areas, obtained via remote sensing, is essential for disaster response, relief, urban and industrial reconstruction, insurance services, and damage assessment. Numerous flood mapping methods have been proposed, each with distinct strengths and limitations. Among the most widely used are machine learning algorithms and spectral indices, though these methods often face challenges, particularly in threshold selection for spectral indices and the sampling process for supervised classification. This study aims to develop an NRT flood mapping approach using supervised classification based on spectral features. The method automatically generates training samples through masks derived from spectral indices. More specifically, this study uses FWEI, NDVI, NDBI, and BSI indices to extract training samples for water/flood, vegetation, built-up areas, and soil, respectively. The Otsu thresholding technique is applied to create the spectral masks. Land cover classification is then performed using the Random Forest algorithm with the automatically generated training samples. The final flood map is obtained by subtracting the pre-flood water class from the post-flood image. The proposed method is implemented using optical satellite images from Sentinel-2, Landsat-8, and Landsat-9. The proposed method's accuracy is rigorously evaluated and compared with those obtained from spectral indices and machine learning techniques. The suggested approach achieves the highest overall accuracy (OA) of 90.57% and a Kappa Coefficient (KC) of 0.89, surpassing SVM (OA: 90.04%, KC: 0.88), Decision Trees (OA: 88.64%, KC: 0.87), and spectral indices like AWEI (OA: 84.12%, KC: 0.82), FWEI (OA: 88.23%, KC: 0.86), NDWI (OA: 85.78%, KC: 0.84), and MNDWI (OA: 87.67%, KC: 0.85). These results underscore the superior accuracy and effectiveness of the proposed approach for NRT flood detection and monitoring using multi-sensor optical imagery.

The threat power transmission and distribution projects pose to the ecological environment has been widely discussed by researchers. The scarcity of early environmental monitoring and supervision technologies, particularly the lack of effective real-time monitoring mechanisms and feedback systems, has hindered the timely quantitative identification of potential early-stage environmental risks. This study aims to comprehensively review the literature and analyze the research context and shortcomings of the advance warning technologies of power transmission and distribution projects construction period using the integrated space-sky-ground system approach. The key contributions of this research include (1) listing ten environmental risks and categorizing the environmental risks associated with the construction cycle of power transmission and distribution projects; (2) categorizing the monitoring data into one-dimensional, two-dimensional, and three-dimensional frameworks; and (3) constructing the potential environmental risk knowledge system by employing the knowledge graph technology and visualizing it. This review study provides a panoramic view of knowledge in a certain field and reveals the issues that have not been fully explored in the research field of monitoring technologies for potential environmental damage caused by power transmission and transformation projects.

Existing fully-automatic transplanters suffer from issues such as low accuracy in conveying and positioning seedling trays, inefficient picking and throwing due to complex movement paths, and seedling damage during the picking process. To address these challenges, this study presents innovative devices for seedling conveying in the X-direction and seedling picking in the Y-direction, considering both row and longitudinal intervals, which simplifies the mechanical structure. Based on these devices, methods were developed to achieve precise seedling positioning in both X and Y directions using multi-sensor combinations and motor control. A Finite State Machine (FSM) model was employed to propose a cooperative method for conveying and picking seedlings, simplifying the execution order and enabling continuous action without dragging or injuring the remaining seedlings. Experimental validation using 72-hole trays demonstrated that positioning deviation increased with motor pulse frequency, with a maximum deviation of 1.35 mm at 800 Hz, which remains within operational requirements. The qualification rate of seedling positioning was 100 % under various transmission speeds. The soil damage ratio (sDR) was measured to evaluate picking success, revealing an average successful seedling extraction rate of 95 %. These research findings offer technical support for efficient coordination between seedling conveying and picking in automatic transplanters.

Drought can have significant impacts on forests, with long periods of water scarcity leading to water stress in trees and possible damages to their eco-physiological functions. Satellite-based remote sensing offers a valuable tool for monitoring and assessing drought conditions over large and remote forested regions. The objective of this study is to evaluate the hydrological consistency in the context of drought of precipitation, soil moisture, evapotranspiration, and land surface temperature observations against in situ measurements in a number of well-monitored sites in New Zealand. Results showed that drought indicators were better captured from soil moisture observations compared to precipitation satellite observations. Nevertheless, we found statistically significant causality relationships between the multi-sensor satellite observations (median p-values ranging from 0.001 to 0.019), with spatial resolution appearing to be an important aspect for the adequate estimation of drought characteristics. Understanding the limitations and capabilities of satellite observations is crucial for improving the accuracy of forest drought monitoring, which, in turn, will aid in sustainable forest management and the development of mitigation and adaptation strategies in the face of changing climate conditions.