Spatio-temporal analysis of litterfall load in the lower reaches of Qarqan and Tarim rivers using BP neural networks.

Litterfall load is crucial in maintaining ecosystem health, controlling wildfires, and estimating carbon stock in arid regions. However, there is a lack of spatiotemporal analysis of litterfall in arid riparian forests. This study aims to estimate Litterfall load using a BP neural network based on vegetation indices from Landsat 5 and 8 satellite images, litterfall inventory data, slope, and distance to major river tributaries.

Contribution of agricultural land conversion to global GHG emissions: A meta-analysis.

Greenhouse gases (GHG) have extensive environmental effects by trapping heat and causing climate change and air pollution. Land plays a key role in the global cycles of GHG (i.e.

Challenges and solutions to biodiversity conservation in arid lands.

The strategic goals of the United Nations and the Aichi Targets for biodiversity conservation have not been met. Instead, biodiversity has continued to rapidly decrease, especially in developing countries. Setting a new global biodiversity framework requires clarifying future priorities and strategies to bridge challenges and provide representative solutions.

Spatio-seasonal variation of water quality influenced by land use and land cover in Lake Muhazi.

Understanding the influence of land use/land cover (LULC) on water quality is pertinent to sustainable water management. This study aimed at assessing the spatio-seasonal variation of water quality in relation to land use types in Lake Muhazi, Rwanda. The National Sanitation Foundation Water Quality Index (NSF-WQI) was used to evaluate the anthropogenically-induced water quality changes.