# GIS-Based Environmental Monitoring and Analysis
## 2 An Overview of Published Articles  
Modern technologies and advanced data analysis algorithms allow the efficient use of geospatial resources. An ever-growing body of available geospatial data holds great potential for addressing pressing challenges such as climate change or major societal and economic transformations. However, a significant proportion of these data remains unused. This article aims to illustrate the practical applicability of harnessing these data in the future to advance the spatial analysis of GIS. The research collected in this issue offers practical implications for environmental policy makers, planners, and stakeholders involved in the development of technologies and tools, ultimately promoting the building of more sustainable and resilient landscapes. The first topic covered in this issue pertains to the modeling and evaluation of factors influencing forest ecosystems [1; 2; 3; 4; 5; 6]. A precise estimation of net primary production (NPP) serves as a crucial indicator of the forest ecosystem's carbon balance [7; 8]. In this research [7], the Boreal Ecosystem Productivity Simulation (BEPS) model was employed to simulate the NPP of the Saihanba mechanized forest farm in 2020 and to investigate the underlying drivers of NPP. Various datasets required for the model, including meteorological information, forest cover, and leaf area index (LAI), along with validation data, were gathered through field studies or sourced from different databases. The findings indicated that, at the flux tower scale, the diurnal variation in net primary production peaked in June. The highest monthly average latent heat flux was observed in June, whereas the sensible heat flux reached its maximum in March. It was also noted that temperatures beneath the canopy tended to be higher than both above-canopy and air temperatures. Additionally, net primary production was particularly responsive to LAI and topographical variables influencing NPP. The average NPP values were found to be greater on shady and gently sloping terrains. A key component of GIS-based analysis is leveraging available resources to safeguard infrastructure against potential hazards. The primary goal is to pinpoint areas susceptible to soil erosion through morphometric analysis and watershed characteristics [9; 10; 11; 12; 13]. Soil erosion presents significant challenges to both human activities and the environment, making it a globally relevant issue. Examining morphometric indices provides valuable insights into watershed geomorphology, which is crucial for predicting and evaluating the behavior of various natural hazards. Effective and sustainable watershed management requires identifying and prioritizing critical sub-catchments. This study focuses on assessing 15 sub-catchments within the Wadi Haly catchment in Saudi Arabia using GIS tools and multiple parameters to guide both immediate and long-term watershed management strategies. By integrating several morphometric indices, each sub-catchment was categorized into three distinct levels of erosion risk. The study concludes that implementing mitigation measures such as contour plowing, terracing, filter strips, and a combination of structural and non-structural solutions can help reduce severe erosion. Another crucial aspect of hazard assessment involves using geomorphic indicators to evaluate relative tectonic activity [14; 15; 16; 17; 18]. This research employs the analysis of geomorphic indices to assess the relative tectonic activity of a coastal region along the Red Sea. This methodology is particularly effective for examining landscape evolution and geomorphic patterns. By conducting an in-depth analysis of various indices, active and inactive features within the study area can be identified and distinguished. The indices utilized in this study include the rock strength index, stream length gradient index, hypsometric integral, catchment analysis index, mountain front sinuosity index, and the ratio of valley bottom width to valley bottom height.