Lightweight Deep Learning Model, ConvNeXt-U: An Improved U-Net Network for Extracting Cropland in Complex Landscapes from Gaofen-2 Images.

Extracting fragmented cropland is essential for effective cropland management and sustainable agricultural development. However, extracting fragmented cropland presents significant challenges due to its irregular and blurred boundaries, as well as the diversity in crop types and distribution. Deep learning methods are widely used for land cover classification.

A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle.

Introduction: The Cinnamomum Camphora var. Borneol (CCB) tree is a valuable timber species with significant medicinal importance, widely cultivated in mountainous areas but susceptible to pests and diseases, making manual surveillance costly.

Methods: This paper proposes a method for detecting CCB pests and diseases using Unmanned aerial vehicle (UAV) as an advanced data collection carrier, capable of gathering large-scale data.

[Spatio-temporal Variation in Monthly Vegetation NPP and Its Responses to Climate and Terrain Factors in Chang-Zhu-Tan Green Heart Area].

The Chang-Zhu-Tan ecological Green Heart area is the largest urban agglomeration Green Heart area in China. To clarify the spatiotemporal changes and driving factors of net primary productivity （NPP） of vegetation in the Chang-Zhu-Tan Green Heart area, an improved Carnegie Ames Stanford Approach （CASA） model was used to estimate the monthly vegetation NPP from 2011 to 2020 based on measured and remote sensing data. With the help of ArcGIS 10.

Non-stationarity of runoff and sediment load and its drivers under climate change and anthropogenic activities in Dongting Lake Basin.

Analysing non-stationarity in runoff and sediment load is crucial for effective water resource management in the Dongting Lake basin amid climate change and human impacts. Using the Mann-Kendall test, Generalized Additive Models for Location, Scale, and Shape framework, and Random Forest models, we evaluated non-stationarity and its drivers in the annual runoff and annual sediment load series at eight hydrological stations from 1961 to 2021. These stations include three inflow sites at the Jingjiang Three Outlets (Ouchi, Songzi, and Hudu Rivers), four inflow sites in the Four Rivers basin (Xiang, Zi, Yuan, and Li Rivers), and one outflow site at Chenglingji.

Experiment and Analysis of Variance for Stabilizing Fine-Grained Soils with Cement and Sawdust Ash as Liner Materials.

Due to volume change and low strength, fine-grained soils are problematic in construction. Stabilization with cement and sawdust ash (SDA) by-products can improve engineering properties. This study aimed to investigate the effectiveness of cement and sawdust ash (SDA) in stabilizing fine-grained soils for liner applications.

Threshold effect of ecosystem services in response to human activity in China's urban agglomeration: a perspective on quantifying ecological resilience.

Ecological resilience reflects the role of human activity intensity (HMI) on regional ecosystem services (ESs), and resilience improvement is crucial for the high-quality development of urban agglomeration areas. However, a theoretical framework for ecological resilience needs to be developed based on ES thresholds under human activities. Based on the threshold index, we used threshold regression model to determine of the nonlinear dominant factors affecting ESs and to identify the priority areas for ecological restoration.

Global prediction of gross primary productivity under future climate change.

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate.