GIS-Based Environmental Monitoring and Analysis

1. Introduction

The environment is an important aspect of sustainable development. Environmental degradation, global warming, forest degradation, agricultural crop production shortfalls, and glacier coverage changes are severe problems that require us to take immediate action. Timely and effective monitoring of the environment enables us to further our knowledge regarding the current status of these conditions, and GIS is a powerful tool that provides vital support for the achievement of sustainable development goals.

Geographic Information Systems (GIS) have extensive applications in environmental monitoring, data analysis, and advanced geosciences. They facilitate effective land use management, soil monitoring, digital soil mapping, and the analysis of erosion, landslides, terrain slopes, and vegetation. In forestry, GIS supports forest monitoring, fire management, the creation of digital elevation models, and the assessment of flammability indices. This technology is also invaluable in managing natural resources and analyzing natural disasters such as floods, droughts, and earthquakes, contributing to disaster risk reduction. The integration of technologies like LIDAR, remote sensing, 2D and 3D analyses, GeoAI, smart city applications, GIS software and devices, spatiotemporal patterns, WebGIS, and mobile and distributed GIS enables advanced data processing and analysis. In precision agriculture and offshore aquaculture, GIS optimizes production processes. For water and ocean monitoring—including oceanography, sea-level rise, land–ocean interactions, coastal environment monitoring, coastal changes, erosion, and water quality—GIS provides essential analytical tools. Additionally, these systems are employed in monitoring climate change, biodiversity loss, air quality, and particulate matter (PM) levels. GIS-related image processing techniques, big data management, data mining, multi-temporal analysis, decision support, and visualization techniques play a crucial role in effective environmental management.

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. The findings present a composite index of relative tectonic activity (Rta), classifying the study area into three categories: low, moderate, and high tectonic activity. Studies on active tectonics in this part of the eastern Red Sea coast of Saudi Arabia remain limited, making this an ideal location for evaluating and modeling relative tectonic activity through large-scale basin analysis. The research confirms that regions with relatively high tectonic activity align with elevated Rta index values, supporting the hypothesis that tectonic activity levels can be effectively inferred from these geomorphic indicators.

The next paper [19] explores the necessity of restoring riverbanks and stream edges as a natural method for mitigating future flood risks. The study examines how spatial resolution influences the mapping of suitable areas for nature-based solutions (NBS) across Europe. To support decision-making, an NBS toolkit was created to pinpoint locations with high potential for these solutions. This tool operates based on suitability mapping, which is increasingly used as a preliminary assessment method for NBS site selection. The toolkit utilizes publicly available European datasets at various spatial resolutions. In this research, a GIS-based approach was employed to assess how different resolutions affect the accuracy of suitability maps. Findings indicate that for large-scale interventions like riparian forest buffers, coarser-resolution datasets are adequate, offering advantages in computational efficiency. On the other hand, high-resolution data proves more beneficial for urban-scale assessments and smaller, localized nature-based solutions [20,21,22,23,24,25,26].

Another study focuses on the application of airborne laser scanning (ALS) technology and advanced analytical methods to detect trees at risk of falling onto highways [27,28]. The paper presents the use of ALS point cloud data in combination with wind speed and direction measurements in Poland to evaluate potential hazards [29,30]. Two techniques, PyCrown and OPALS, were implemented to determine tree crown heights [31,32]. Subsequently, the influence of wind direction on tree-related risks was analyzed, leading to the identification of trees with the potential to fall onto roadways. Among the methods tested, OPALS demonstrated the highest accuracy in detecting hazardous trees. The findings underscore the effectiveness of integrating ALS technology with advanced algorithms and meteorological data to enhance the identification of potential roadside hazards.

Modern technological advancements provide valuable opportunities for studying urban forestry [33,34,35,36,37]. A growing trend in urban analysis is the implementation of the 3-30-300 model for green cities, which was examined in a case study conducted in Warsaw, Poland [38]. This concept, introduced by Konijnendijk [39,40], defines essential criteria for a “green city”, suggesting that every residence should have a view of at least three trees, 30% of the city should be covered in vegetation, and the nearest park or forest should be within 300 m. The research aimed to evaluate whether Warsaw aligns with these standards and can be classified as a green city. The results indicate that while Warsaw exhibits a relatively high level of greenery, certain areas still fall short of meeting all three criteria.

This issue also presents a study on urban expansion modeling using remote sensing techniques. The research [41] integrates the analytic hierarchy process (AHP) with Geographic Information System (GIS) methods to identify suitable zones for urban development [42,43,44,45,46] across six districts of the Mersin Metropolitan Area in Turkey. The primary goal is to generate a land suitability map that can guide strategic urban planning decisions. The study leverages freely available Landsat satellite imagery and employs the Random Forest (RF) algorithm to analyze land use and land cover (LULC) changes over a 15-year period. A novel contribution of this research is the incorporation of the suitability map into an urban growth simulation, developed using a logistic regression (LR) model. This simulation projects urban expansion trends through 2027, enabling planners to assess potential development areas against established suitability criteria. The findings highlight spatial patterns in land suitability and expected urban growth, assisting policymakers in selecting appropriate development sites while preserving ecological balance. The study particularly emphasizes the necessity of considering multiple factors—including topography, accessibility, soil capacity, and geological characteristics—in the urban planning process.

3. Conclusions

Mapping environmental change plays a crucial role in land-use planning and management. Landscapes are subject to dynamic change and geo-information techniques have proven to be valuable tools for obtaining consistent and accurate information about the characteristics and condition of land surfaces. Continuous monitoring is essential to track these developments and ensure that land information remains up to date. Whether at the local or global level, land cover data support the effective allocation and use of the Earth’s limited resources. The wide range of technologies available enables the detection of various surface properties, including moisture content, biochemical composition, and structural features.

Comprehensive GIS analysis provides opportunities to manage land suitability and predict potential hazards. Dynamic monitoring of environmental resources is an integral part of resource management and maintaining ecosystem stability, and GIS tools are useful and necessary for these goals. The future will present cutting-edge research focusing on land cover mapping and the dynamics of environmental change using GIS techniques. Research of interest will include studies ranging from regional to global scales, the use of passive and active sensors, and advances in the use of technology and geoinformation methods.

An ever-growing body of publicly available land use and land cover data offers great potential for addressing pressing challenges such as those associated with climate change or profound social and economic transformations. The new GIS approaches have broad applicability and adaptability and should encourage the exploration of untapped potential in open data. Other benefits include the objectification and optimization of planning processes in environmental studies.