Wetlands are critical ecosystems that deliver essential services such as water purification, flood regulation, and biodiversity support. Non-intertidal wetlands, in particular, play a key role in maintaining global ecological balance. However, due to their complex structures and dynamic nature, monitoring these ecosystems poses significant challenges. This study offers a comprehensive review of current remote sensing techniques applied to non-intertidal wetlands, with a focus on classification and mapping, vegetation and biodiversity assessment, hydrological monitoring, water quality evaluation, spatiotemporal dynamics, and ecosystem service quantification. We further examine the diverse data sources, analytical models, and technical tools used to monitor these ecosystems. Key challenges identified include limitations in spatial and temporal resolution, ecosystem complexity, and the difficulties of large-scale monitoring. To overcome these barriers, the paper explores promising future directions, including multi-source data integration, the advanced use of hyperspectral satellites, the application of artificial intelligence and machine learning, the coupling of ecological models with socio-economic data, and the development of a global non-intertidal wetland monitoring system. These strategies hold the potential to enhance scientific understanding, support effective conservation efforts, and facilitate sustainable management practices for wetland ecosystems.

Electricity reliability is essential for modern socioeconomic development, yet global efforts have often prioritized expanding access over ensuring service quality. The critical dimension of service reliability creates a significant policy challenge in many developing nations, especially in South Africa which has experienced reliability crisis driven by frequent load shedding, weather shocks and aging assets. In this study, we developed a multiscale framework that uses Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights from 2012 to 2024 as a proxy to explore electricity reliability with complementary indicators. We used index of dispersion, z-score, and annual cycling as the reliability indicators to explore the power instability in South Africa from pixel level to administrative units. Our results show strong positive correlations between the national radiance z-score and the power generation availability (r = 0.90), and an inverse relationship with power outage metrics (r = -0.90). Spatially, Emerging Hot Spot Analysis reveals significant clustering of instability, identifying hot spots in provinces such as Gauteng and Western Cape, contrasting with cold spots in Limpopo and North West. The annual cycling pattern is pronounced with a winter-peak in Free State, Gauteng, Mpumalanga, and North West. Load-shedding stages can dampen the annual cycling pattern as validated in Cape Town. In addition, precipitation has a modest contemporaneous dimming effect that fades by one to two months. Our findings indicate that nighttime lights can support an operational and policy-oriented dashboard for monitoring reliability across space and time.

Abstract Spatial analysis can generate both exogenous and endogenous bia ses, which will lead to ethics issues. Exogenous biases arise from external factors or environments and are unrelated to internal operating mechanisms, while endogenous biases stem from internal processes or technologies. Although much attention has been given to exogenous biases, endogenous biases in spatial analysis have been largely overlooked, and a comprehensive methodology for addressing them is yet to be developed. To tackle this challenge, we propose that visual analytics can play a key role in understanding geographic data and improving the interpretation of analytical results. In this study, we conducted a preliminary investigation using various visualization techniques to explore endogenous biases. Our findings demonstrate the potential of visual analytics to uncover hidden biases and identify associated issues. Additionally, we synthesized these visualization strategies into a framework that approximates a method for detecting endogenous biases. We conducted a user study to validate the effectiveness of the proposed framework. Through this work, we advocate for the integration of visualization at three critical stages of spatial analysis in order to minimize errors, address ethical concerns, and reduce misinterpretations associated with endogenous biases.

Abstract Although Global Digital Elevation Models (GDEMs) are widely used, their spatial error distributions over extensive regions remain insufficiently understood. Using ICESat-2 Reference Control Points (RCPs) (~ 30 points/km²), this paper not only evaluates GDEMs with point-based metrics such as Root Mean Square Error (RMSE) and Mean Error (ME), but also proposes a novel raster-independent Spatial Error Mapping (SEM) approach. The proposed SEM leverages dense RCPs to generate two complementary maps: the Spatial Accuracy Map (SAM) and the Spatial Bias Map (SBM). Results show that FABDEM consistently exhibits the highest vertical accuracy (RMSE = 2.06 m; ME = -1.01 m), followed by AW3D30 (3.47 m; -2.31 m), NASADEM (3.59 m; -1.33 m), SRTM (3.90 m; -1.22 m), and ASTER GDEM (6.58 m; -0.89 m). The results reveal three key patterns: (1) accuracy generally decreases with increasing elevation and slope; (2) most GDEMs exhibit higher errors on north- and northwest-facing slopes; and (3) forested and urban areas show the lowest overall accuracy. The SEM analysis uncovered continuous spatial error patterns in GDEMs such as localized biases and processing- or acquisition-geometry-related artifacts in the SBM and SAM that were previously unreported in the literature and are not captured by traditional point-based metrics.